Student Wyckoff RS Symbol/MarketRelative Strength Indicator STUDENT WYCKOFF RS SYMBOL/MARKET
Description
The Relative Strength (RS) Indicator compares the price performance of the current financial instrument (e.g., a stock) against another instrument (e.g., an index or another stock). It is calculated by dividing the closing price of the first instrument by the closing price of the second, then multiplying by 100. This provides a percentage ratio that shows how one instrument outperforms or underperforms another. The indicator helps traders identify strong or weak assets, spot market leaders, or evaluate an asset’s performance relative to a benchmark.
Key Features
Relative Strength Calculation: Divides the closing price of the current instrument by the closing price of the second instrument and multiplies by 100 to express the ratio as a percentage.
Simple Moving Average (SMA): Applies a customizable Simple Moving Average (default period: 14) to smooth the data and highlight trends.
Visualization: Displays the Relative Strength as a blue line, the SMA as an orange line, and colors bars (blue for rising, red for falling) to indicate changes in relative strength.
Flexibility: Allows users to select the second instrument via an input field and adjust the SMA period.
Applications
Market Comparison: Assess whether a stock is outperforming an index (e.g., S&P 500 or MOEX) to identify strong assets for investment.
Sector Analysis: Compare stocks within a sector or against a sector ETF to pinpoint leaders.
Trend Analysis: Use the rise or fall of the RS line and its SMA to gauge the strength of an asset’s trend relative to another instrument.
Trade Timing: Bar coloring helps quickly identify changes in relative strength, aiding short-term trading decisions.
Interpretation
Rising RS: Indicates the first instrument is outperforming the second (e.g., a stock growing faster than an index).
Falling RS: Suggests the first instrument is underperforming.
SMA as a Trend Filter: If the RS line is above the SMA, it may signal strengthening performance; if below, weakening performance.
Settings
Instrument 2: Ticker of the second instrument (default: QQQ).
SMA Period: Period for the Simple Moving Average (default: 14).
Notes
The indicator works on any timeframe but requires accurate ticker input for the second instrument.
Ensure data for both instruments is available on the selected timeframe for precise analysis.
在腳本中搜尋"100年黄金价格走势"
Multi SMA + Golden/Death + Heatmap + BB**Multi SMA (50/100/200) + Golden/Death + Candle Heatmap + BB**
A practical trend toolkit that blends classic 50/100/200 SMAs with clear crossover labels, special 🚀 Golden / 💀 Death Cross markers, and a readable candle heatmap based on a dynamic regression midline and volatility bands. Optional Bollinger Bands are included for context.
* See trend direction at a glance with SMAs.
* Get minimal, de-cluttered labels on important crosses (50↔100, 50↔200, 100↔200).
* Highlight big regime shifts with special Golden/Death tags.
* Read momentum and volatility with the candle heatmap.
* Add Bollinger Bands if you want classic mean-reversion context.
Designed to be lightweight, non-repainting on confirmed bars, and flexible across timeframes.
# What This Indicator Does (plain English)
* **Tracks trend** using **SMA 50/100/200** and lets you optionally compute each SMA on a higher or different timeframe (HTF-safe, no lookahead).
* **Prints labels** when SMAs cross each other (up or down). You can force signals only after bar close to avoid repaint.
* **Marks Golden/Death Crosses** (50 over/under 200) with special labels so major regime changes stand out.
* **Colors candles** with a **heatmap** built from a regression midline and volatility bands—greenish above, reddish below, with a smooth gradient.
* **Optionally shows Bollinger Bands** (basis SMA + stdev bands) and fills the area between them.
* **Includes alert conditions** for Golden and Death Cross so you can automate notifications.
---
# Settings — Simple Explanations
## Source
* **Source**: Price source used to calculate SMAs and Bollinger basis. Default: `close`.
## SMA 50
* **Show 50**: Turn the SMA(50) line on/off.
* **Length 50**: How many bars to average. Lower = faster but noisier.
* **Color 50** / **Width 50**: Visual style.
* **Timeframe 50**: Optional alternate timeframe for SMA(50). Leave empty to use the chart timeframe.
## SMA 100
* **Show 100**: Turn the SMA(100) line on/off.
* **Length 100**: Bars used for the mid-term trend.
* **Color 100** / **Width 100**: Visual style.
* **Timeframe 100**: Optional alternate timeframe for SMA(100).
## SMA 200
* **Show 200**: Turn the SMA(200) line on/off.
* **Length 200**: Bars used for the long-term trend.
* **Color 200** / **Width 200**: Visual style.
* **Timeframe 200**: Optional alternate timeframe for SMA(200).
## Signals (crossover labels)
* **Show crossover signals**: Prints triangle labels on SMA crosses (50↔100, 50↔200, 100↔200).
* **Wait for bar close (confirmed)**: If ON, signals only appear after the candle closes (reduces repaint).
* **Min bars between same-pair signals**: Minimum spacing to avoid duplicate labels from the same SMA pair too often.
* **Trend filter (buy: 50>100>200, sell: 50<100<200)**: Only show bullish labels when SMAs are stacked bullish (50 above 100 above 200), and only show bearish labels when stacked bearish.
### Label Offset
* **Offset mode**: Choose how to push labels away from price:
* **Percent**: Offset is a % of price.
* **ATR x**: Offset is ATR(14) × multiplier.
* **Percent of price (%)**: Used when mode = Percent.
* **ATR multiplier (for ‘ATR x’)**: Used when mode = ATR x.
### Label Colors
* **Bull color** / **Bear color**: Background of triangle labels.
* **Bull label text color** / **Bear label text color**: Text color inside the triangles.
## Golden / Death Cross
* **Show 🚀 Golden Cross (50↑200)**: Show a special “Golden” label when SMA50 crosses above SMA200.
* **Golden label color** / **Golden text color**: Styling for Golden label.
* **Show 💀 Death Cross (50↓200)**: Show a special “Death” label when SMA50 crosses below SMA200.
* **Death label color** / **Death text color**: Styling for Death label.
## Candle Heatmap
* **Enable heatmap candle colors**: Turns the heatmap on/off.
* **Length**: Lookback for the regression midline and volatility measure.
* **Deviation Multiplier**: Band width around the midline (bigger = wider).
* **Volatility basis**:
* **RMA Range** (smoothed high-low range)
* **Stdev** (standard deviation of close)
* **Upper/Middle/Lower color**: Gradient colors for the heatmap.
* **Heatmap transparency (0..100)**: 0 = solid, 100 = invisible.
* **Force override base candles**: Repaint base candles so heatmap stays visible even if your chart has custom coloring.
## Bollinger Bands (optional)
* **Show Bollinger Bands**: Toggle the overlay on/off.
* **Length**: Basis SMA length.
* **StdDev Multiplier**: Distance of bands from the basis in standard deviations.
* **Basis color** / **Band color**: Line colors for basis and bands.
* **Bands fill transparency**: Opacity of the fill between upper/lower bands.
---
# Features & How It Works
## 1) HTF-Safe SMAs
Each SMA can be calculated on the chart timeframe or a higher/different timeframe you choose. The script pulls HTF values **without lookahead** (non-repainting on confirmed bars).
## 2) Crossover Labels (Three Pairs)
* **50↔100**, **50↔200**, **100↔200**:
* **Triangle Up** label when the first SMA crosses **above** the second.
* **Triangle Down** label when it crosses **below**.
* Optional **Trend Filter** ensures only signals aligned with the overall stack (50>100>200 for bullish, 50<100<200 for bearish).
* **Debounce** spacing avoids repeated labels for the same pair too close together.
## 3) Golden / Death Cross Highlights
* **🚀 Golden Cross**: SMA50 crosses **above** SMA200 (often a longer-term bullish regime shift).
* **💀 Death Cross**: SMA50 crosses **below** SMA200 (often a longer-term bearish regime shift).
* Separate styling so they stand out from regular cross labels.
## 4) Candle Heatmap
* Builds a **regression midline** with **volatility bands**; colors candles by their position inside that channel.
* Smooth gradient: lower side → reddish, mid → yellowish, upper side → greenish.
* Helps you see momentum and “where price sits” relative to a dynamic channel.
## 5) Bollinger Bands (Optional)
* Classic **basis SMA** ± **StdDev** bands.
* Light visual context for mean-reversion and volatility expansion.
## 6) Alerts
* **Golden Cross**: `🚀 GOLDEN CROSS: SMA 50 crossed ABOVE SMA 200`
* **Death Cross**: `💀 DEATH CROSS: SMA 50 crossed BELOW SMA 200`
Add these to your alerts to get notified automatically.
---
# Tips & Notes
* For fewer false positives, keep **“Wait for bar close”** ON, especially on lower timeframes.
* Use the **Trend Filter** to align signals with the broader stack and cut noise.
* For HTF context, set **Timeframe 50/100/200** to higher frames (e.g., H1/H4/D) while you trade on a lower frame.
* Heatmap “Length” and “Deviation Multiplier” control smoothness and channel width—tune for your asset’s volatility.
Markov Chain [3D] | FractalystWhat exactly is a Markov Chain?
This indicator uses a Markov Chain model to analyze, quantify, and visualize the transitions between market regimes (Bull, Bear, Neutral) on your chart. It dynamically detects these regimes in real-time, calculates transition probabilities, and displays them as animated 3D spheres and arrows, giving traders intuitive insight into current and future market conditions.
How does a Markov Chain work, and how should I read this spheres-and-arrows diagram?
Think of three weather modes: Sunny, Rainy, Cloudy.
Each sphere is one mode. The loop on a sphere means “stay the same next step” (e.g., Sunny again tomorrow).
The arrows leaving a sphere show where things usually go next if they change (e.g., Sunny moving to Cloudy).
Some paths matter more than others. A more prominent loop means the current mode tends to persist. A more prominent outgoing arrow means a change to that destination is the usual next step.
Direction isn’t symmetric: moving Sunny→Cloudy can behave differently than Cloudy→Sunny.
Now relabel the spheres to markets: Bull, Bear, Neutral.
Spheres: market regimes (uptrend, downtrend, range).
Self‑loop: tendency for the current regime to continue on the next bar.
Arrows: the most common next regime if a switch happens.
How to read: Start at the sphere that matches current bar state. If the loop stands out, expect continuation. If one outgoing path stands out, that switch is the typical next step. Opposite directions can differ (Bear→Neutral doesn’t have to match Neutral→Bear).
What states and transitions are shown?
The three market states visualized are:
Bullish (Bull): Upward or strong-market regime.
Bearish (Bear): Downward or weak-market regime.
Neutral: Sideways or range-bound regime.
Bidirectional animated arrows and probability labels show how likely the market is to move from one regime to another (e.g., Bull → Bear or Neutral → Bull).
How does the regime detection system work?
You can use either built-in price returns (based on adaptive Z-score normalization) or supply three custom indicators (such as volume, oscillators, etc.).
Values are statistically normalized (Z-scored) over a configurable lookback period.
The normalized outputs are classified into Bull, Bear, or Neutral zones.
If using three indicators, their regime signals are averaged and smoothed for robustness.
How are transition probabilities calculated?
On every confirmed bar, the algorithm tracks the sequence of detected market states, then builds a rolling window of transitions.
The code maintains a transition count matrix for all regime pairs (e.g., Bull → Bear).
Transition probabilities are extracted for each possible state change using Laplace smoothing for numerical stability, and frequently updated in real-time.
What is unique about the visualization?
3D animated spheres represent each regime and change visually when active.
Animated, bidirectional arrows reveal transition probabilities and allow you to see both dominant and less likely regime flows.
Particles (moving dots) animate along the arrows, enhancing the perception of regime flow direction and speed.
All elements dynamically update with each new price bar, providing a live market map in an intuitive, engaging format.
Can I use custom indicators for regime classification?
Yes! Enable the "Custom Indicators" switch and select any three chart series as inputs. These will be normalized and combined (each with equal weight), broadening the regime classification beyond just price-based movement.
What does the “Lookback Period” control?
Lookback Period (default: 100) sets how much historical data builds the probability matrix. Shorter periods adapt faster to regime changes but may be noisier. Longer periods are more stable but slower to adapt.
How is this different from a Hidden Markov Model (HMM)?
It sets the window for both regime detection and probability calculations. Lower values make the system more reactive, but potentially noisier. Higher values smooth estimates and make the system more robust.
How is this Markov Chain different from a Hidden Markov Model (HMM)?
Markov Chain (as here): All market regimes (Bull, Bear, Neutral) are directly observable on the chart. The transition matrix is built from actual detected regimes, keeping the model simple and interpretable.
Hidden Markov Model: The actual regimes are unobservable ("hidden") and must be inferred from market output or indicator "emissions" using statistical learning algorithms. HMMs are more complex, can capture more subtle structure, but are harder to visualize and require additional machine learning steps for training.
A standard Markov Chain models transitions between observable states using a simple transition matrix, while a Hidden Markov Model assumes the true states are hidden (latent) and must be inferred from observable “emissions” like price or volume data. In practical terms, a Markov Chain is transparent and easier to implement and interpret; an HMM is more expressive but requires statistical inference to estimate hidden states from data.
Markov Chain: states are observable; you directly count or estimate transition probabilities between visible states. This makes it simpler, faster, and easier to validate and tune.
HMM: states are hidden; you only observe emissions generated by those latent states. Learning involves machine learning/statistical algorithms (commonly Baum–Welch/EM for training and Viterbi for decoding) to infer both the transition dynamics and the most likely hidden state sequence from data.
How does the indicator avoid “repainting” or look-ahead bias?
All regime changes and matrix updates happen only on confirmed (closed) bars, so no future data is leaked, ensuring reliable real-time operation.
Are there practical tuning tips?
Tune the Lookback Period for your asset/timeframe: shorter for fast markets, longer for stability.
Use custom indicators if your asset has unique regime drivers.
Watch for rapid changes in transition probabilities as early warning of a possible regime shift.
Who is this indicator for?
Quants and quantitative researchers exploring probabilistic market modeling, especially those interested in regime-switching dynamics and Markov models.
Programmers and system developers who need a probabilistic regime filter for systematic and algorithmic backtesting:
The Markov Chain indicator is ideally suited for programmatic integration via its bias output (1 = Bull, 0 = Neutral, -1 = Bear).
Although the visualization is engaging, the core output is designed for automated, rules-based workflows—not for discretionary/manual trading decisions.
Developers can connect the indicator’s output directly to their Pine Script logic (using input.source()), allowing rapid and robust backtesting of regime-based strategies.
It acts as a plug-and-play regime filter: simply plug the bias output into your entry/exit logic, and you have a scientifically robust, probabilistically-derived signal for filtering, timing, position sizing, or risk regimes.
The MC's output is intentionally "trinary" (1/0/-1), focusing on clear regime states for unambiguous decision-making in code. If you require nuanced, multi-probability or soft-label state vectors, consider expanding the indicator or stacking it with a probability-weighted logic layer in your scripting.
Because it avoids subjectivity, this approach is optimal for systematic quants, algo developers building backtested, repeatable strategies based on probabilistic regime analysis.
What's the mathematical foundation behind this?
The mathematical foundation behind this Markov Chain indicator—and probabilistic regime detection in finance—draws from two principal models: the (standard) Markov Chain and the Hidden Markov Model (HMM).
How to use this indicator programmatically?
The Markov Chain indicator automatically exports a bias value (+1 for Bullish, -1 for Bearish, 0 for Neutral) as a plot visible in the Data Window. This allows you to integrate its regime signal into your own scripts and strategies for backtesting, automation, or live trading.
Step-by-Step Integration with Pine Script (input.source)
Add the Markov Chain indicator to your chart.
This must be done first, since your custom script will "pull" the bias signal from the indicator's plot.
In your strategy, create an input using input.source()
Example:
//@version=5
strategy("MC Bias Strategy Example")
mcBias = input.source(close, "MC Bias Source")
After saving, go to your script’s settings. For the “MC Bias Source” input, select the plot/output of the Markov Chain indicator (typically its bias plot).
Use the bias in your trading logic
Example (long only on Bull, flat otherwise):
if mcBias == 1
strategy.entry("Long", strategy.long)
else
strategy.close("Long")
For more advanced workflows, combine mcBias with additional filters or trailing stops.
How does this work behind-the-scenes?
TradingView’s input.source() lets you use any plot from another indicator as a real-time, “live” data feed in your own script (source).
The selected bias signal is available to your Pine code as a variable, enabling logical decisions based on regime (trend-following, mean-reversion, etc.).
This enables powerful strategy modularity : decouple regime detection from entry/exit logic, allowing fast experimentation without rewriting core signal code.
Integrating 45+ Indicators with Your Markov Chain — How & Why
The Enhanced Custom Indicators Export script exports a massive suite of over 45 technical indicators—ranging from classic momentum (RSI, MACD, Stochastic, etc.) to trend, volume, volatility, and oscillator tools—all pre-calculated, centered/scaled, and available as plots.
// Enhanced Custom Indicators Export - 45 Technical Indicators
// Comprehensive technical analysis suite for advanced market regime detection
//@version=6
indicator('Enhanced Custom Indicators Export | Fractalyst', shorttitle='Enhanced CI Export', overlay=false, scale=scale.right, max_labels_count=500, max_lines_count=500)
// |----- Input Parameters -----| //
momentum_group = "Momentum Indicators"
trend_group = "Trend Indicators"
volume_group = "Volume Indicators"
volatility_group = "Volatility Indicators"
oscillator_group = "Oscillator Indicators"
display_group = "Display Settings"
// Common lengths
length_14 = input.int(14, "Standard Length (14)", minval=1, maxval=100, group=momentum_group)
length_20 = input.int(20, "Medium Length (20)", minval=1, maxval=200, group=trend_group)
length_50 = input.int(50, "Long Length (50)", minval=1, maxval=200, group=trend_group)
// Display options
show_table = input.bool(true, "Show Values Table", group=display_group)
table_size = input.string("Small", "Table Size", options= , group=display_group)
// |----- MOMENTUM INDICATORS (15 indicators) -----| //
// 1. RSI (Relative Strength Index)
rsi_14 = ta.rsi(close, length_14)
rsi_centered = rsi_14 - 50
// 2. Stochastic Oscillator
stoch_k = ta.stoch(close, high, low, length_14)
stoch_d = ta.sma(stoch_k, 3)
stoch_centered = stoch_k - 50
// 3. Williams %R
williams_r = ta.stoch(close, high, low, length_14) - 100
// 4. MACD (Moving Average Convergence Divergence)
= ta.macd(close, 12, 26, 9)
// 5. Momentum (Rate of Change)
momentum = ta.mom(close, length_14)
momentum_pct = (momentum / close ) * 100
// 6. Rate of Change (ROC)
roc = ta.roc(close, length_14)
// 7. Commodity Channel Index (CCI)
cci = ta.cci(close, length_20)
// 8. Money Flow Index (MFI)
mfi = ta.mfi(close, length_14)
mfi_centered = mfi - 50
// 9. Awesome Oscillator (AO)
ao = ta.sma(hl2, 5) - ta.sma(hl2, 34)
// 10. Accelerator Oscillator (AC)
ac = ao - ta.sma(ao, 5)
// 11. Chande Momentum Oscillator (CMO)
cmo = ta.cmo(close, length_14)
// 12. Detrended Price Oscillator (DPO)
dpo = close - ta.sma(close, length_20)
// 13. Price Oscillator (PPO)
ppo = ta.sma(close, 12) - ta.sma(close, 26)
ppo_pct = (ppo / ta.sma(close, 26)) * 100
// 14. TRIX
trix_ema1 = ta.ema(close, length_14)
trix_ema2 = ta.ema(trix_ema1, length_14)
trix_ema3 = ta.ema(trix_ema2, length_14)
trix = ta.roc(trix_ema3, 1) * 10000
// 15. Klinger Oscillator
klinger = ta.ema(volume * (high + low + close) / 3, 34) - ta.ema(volume * (high + low + close) / 3, 55)
// 16. Fisher Transform
fisher_hl2 = 0.5 * (hl2 - ta.lowest(hl2, 10)) / (ta.highest(hl2, 10) - ta.lowest(hl2, 10)) - 0.25
fisher = 0.5 * math.log((1 + fisher_hl2) / (1 - fisher_hl2))
// 17. Stochastic RSI
stoch_rsi = ta.stoch(rsi_14, rsi_14, rsi_14, length_14)
stoch_rsi_centered = stoch_rsi - 50
// 18. Relative Vigor Index (RVI)
rvi_num = ta.swma(close - open)
rvi_den = ta.swma(high - low)
rvi = rvi_den != 0 ? rvi_num / rvi_den : 0
// 19. Balance of Power (BOP)
bop = (close - open) / (high - low)
// |----- TREND INDICATORS (10 indicators) -----| //
// 20. Simple Moving Average Momentum
sma_20 = ta.sma(close, length_20)
sma_momentum = ((close - sma_20) / sma_20) * 100
// 21. Exponential Moving Average Momentum
ema_20 = ta.ema(close, length_20)
ema_momentum = ((close - ema_20) / ema_20) * 100
// 22. Parabolic SAR
sar = ta.sar(0.02, 0.02, 0.2)
sar_trend = close > sar ? 1 : -1
// 23. Linear Regression Slope
lr_slope = ta.linreg(close, length_20, 0) - ta.linreg(close, length_20, 1)
// 24. Moving Average Convergence (MAC)
mac = ta.sma(close, 10) - ta.sma(close, 30)
// 25. Trend Intensity Index (TII)
tii_sum = 0.0
for i = 1 to length_20
tii_sum += close > close ? 1 : 0
tii = (tii_sum / length_20) * 100
// 26. Ichimoku Cloud Components
ichimoku_tenkan = (ta.highest(high, 9) + ta.lowest(low, 9)) / 2
ichimoku_kijun = (ta.highest(high, 26) + ta.lowest(low, 26)) / 2
ichimoku_signal = ichimoku_tenkan > ichimoku_kijun ? 1 : -1
// 27. MESA Adaptive Moving Average (MAMA)
mama_alpha = 2.0 / (length_20 + 1)
mama = ta.ema(close, length_20)
mama_momentum = ((close - mama) / mama) * 100
// 28. Zero Lag Exponential Moving Average (ZLEMA)
zlema_lag = math.round((length_20 - 1) / 2)
zlema_data = close + (close - close )
zlema = ta.ema(zlema_data, length_20)
zlema_momentum = ((close - zlema) / zlema) * 100
// |----- VOLUME INDICATORS (6 indicators) -----| //
// 29. On-Balance Volume (OBV)
obv = ta.obv
// 30. Volume Rate of Change (VROC)
vroc = ta.roc(volume, length_14)
// 31. Price Volume Trend (PVT)
pvt = ta.pvt
// 32. Negative Volume Index (NVI)
nvi = 0.0
nvi := volume < volume ? nvi + ((close - close ) / close ) * nvi : nvi
// 33. Positive Volume Index (PVI)
pvi = 0.0
pvi := volume > volume ? pvi + ((close - close ) / close ) * pvi : pvi
// 34. Volume Oscillator
vol_osc = ta.sma(volume, 5) - ta.sma(volume, 10)
// 35. Ease of Movement (EOM)
eom_distance = high - low
eom_box_height = volume / 1000000
eom = eom_box_height != 0 ? eom_distance / eom_box_height : 0
eom_sma = ta.sma(eom, length_14)
// 36. Force Index
force_index = volume * (close - close )
force_index_sma = ta.sma(force_index, length_14)
// |----- VOLATILITY INDICATORS (10 indicators) -----| //
// 37. Average True Range (ATR)
atr = ta.atr(length_14)
atr_pct = (atr / close) * 100
// 38. Bollinger Bands Position
bb_basis = ta.sma(close, length_20)
bb_dev = 2.0 * ta.stdev(close, length_20)
bb_upper = bb_basis + bb_dev
bb_lower = bb_basis - bb_dev
bb_position = bb_dev != 0 ? (close - bb_basis) / bb_dev : 0
bb_width = bb_dev != 0 ? (bb_upper - bb_lower) / bb_basis * 100 : 0
// 39. Keltner Channels Position
kc_basis = ta.ema(close, length_20)
kc_range = ta.ema(ta.tr, length_20)
kc_upper = kc_basis + (2.0 * kc_range)
kc_lower = kc_basis - (2.0 * kc_range)
kc_position = kc_range != 0 ? (close - kc_basis) / kc_range : 0
// 40. Donchian Channels Position
dc_upper = ta.highest(high, length_20)
dc_lower = ta.lowest(low, length_20)
dc_basis = (dc_upper + dc_lower) / 2
dc_position = (dc_upper - dc_lower) != 0 ? (close - dc_basis) / (dc_upper - dc_lower) : 0
// 41. Standard Deviation
std_dev = ta.stdev(close, length_20)
std_dev_pct = (std_dev / close) * 100
// 42. Relative Volatility Index (RVI)
rvi_up = ta.stdev(close > close ? close : 0, length_14)
rvi_down = ta.stdev(close < close ? close : 0, length_14)
rvi_total = rvi_up + rvi_down
rvi_volatility = rvi_total != 0 ? (rvi_up / rvi_total) * 100 : 50
// 43. Historical Volatility
hv_returns = math.log(close / close )
hv = ta.stdev(hv_returns, length_20) * math.sqrt(252) * 100
// 44. Garman-Klass Volatility
gk_vol = math.log(high/low) * math.log(high/low) - (2*math.log(2)-1) * math.log(close/open) * math.log(close/open)
gk_volatility = math.sqrt(ta.sma(gk_vol, length_20)) * 100
// 45. Parkinson Volatility
park_vol = math.log(high/low) * math.log(high/low)
parkinson = math.sqrt(ta.sma(park_vol, length_20) / (4 * math.log(2))) * 100
// 46. Rogers-Satchell Volatility
rs_vol = math.log(high/close) * math.log(high/open) + math.log(low/close) * math.log(low/open)
rogers_satchell = math.sqrt(ta.sma(rs_vol, length_20)) * 100
// |----- OSCILLATOR INDICATORS (5 indicators) -----| //
// 47. Elder Ray Index
elder_bull = high - ta.ema(close, 13)
elder_bear = low - ta.ema(close, 13)
elder_power = elder_bull + elder_bear
// 48. Schaff Trend Cycle (STC)
stc_macd = ta.ema(close, 23) - ta.ema(close, 50)
stc_k = ta.stoch(stc_macd, stc_macd, stc_macd, 10)
stc_d = ta.ema(stc_k, 3)
stc = ta.stoch(stc_d, stc_d, stc_d, 10)
// 49. Coppock Curve
coppock_roc1 = ta.roc(close, 14)
coppock_roc2 = ta.roc(close, 11)
coppock = ta.wma(coppock_roc1 + coppock_roc2, 10)
// 50. Know Sure Thing (KST)
kst_roc1 = ta.roc(close, 10)
kst_roc2 = ta.roc(close, 15)
kst_roc3 = ta.roc(close, 20)
kst_roc4 = ta.roc(close, 30)
kst = ta.sma(kst_roc1, 10) + 2*ta.sma(kst_roc2, 10) + 3*ta.sma(kst_roc3, 10) + 4*ta.sma(kst_roc4, 15)
// 51. Percentage Price Oscillator (PPO)
ppo_line = ((ta.ema(close, 12) - ta.ema(close, 26)) / ta.ema(close, 26)) * 100
ppo_signal = ta.ema(ppo_line, 9)
ppo_histogram = ppo_line - ppo_signal
// |----- PLOT MAIN INDICATORS -----| //
// Plot key momentum indicators
plot(rsi_centered, title="01_RSI_Centered", color=color.purple, linewidth=1)
plot(stoch_centered, title="02_Stoch_Centered", color=color.blue, linewidth=1)
plot(williams_r, title="03_Williams_R", color=color.red, linewidth=1)
plot(macd_histogram, title="04_MACD_Histogram", color=color.orange, linewidth=1)
plot(cci, title="05_CCI", color=color.green, linewidth=1)
// Plot trend indicators
plot(sma_momentum, title="06_SMA_Momentum", color=color.navy, linewidth=1)
plot(ema_momentum, title="07_EMA_Momentum", color=color.maroon, linewidth=1)
plot(sar_trend, title="08_SAR_Trend", color=color.teal, linewidth=1)
plot(lr_slope, title="09_LR_Slope", color=color.lime, linewidth=1)
plot(mac, title="10_MAC", color=color.fuchsia, linewidth=1)
// Plot volatility indicators
plot(atr_pct, title="11_ATR_Pct", color=color.yellow, linewidth=1)
plot(bb_position, title="12_BB_Position", color=color.aqua, linewidth=1)
plot(kc_position, title="13_KC_Position", color=color.olive, linewidth=1)
plot(std_dev_pct, title="14_StdDev_Pct", color=color.silver, linewidth=1)
plot(bb_width, title="15_BB_Width", color=color.gray, linewidth=1)
// Plot volume indicators
plot(vroc, title="16_VROC", color=color.blue, linewidth=1)
plot(eom_sma, title="17_EOM", color=color.red, linewidth=1)
plot(vol_osc, title="18_Vol_Osc", color=color.green, linewidth=1)
plot(force_index_sma, title="19_Force_Index", color=color.orange, linewidth=1)
plot(obv, title="20_OBV", color=color.purple, linewidth=1)
// Plot additional oscillators
plot(ao, title="21_Awesome_Osc", color=color.navy, linewidth=1)
plot(cmo, title="22_CMO", color=color.maroon, linewidth=1)
plot(dpo, title="23_DPO", color=color.teal, linewidth=1)
plot(trix, title="24_TRIX", color=color.lime, linewidth=1)
plot(fisher, title="25_Fisher", color=color.fuchsia, linewidth=1)
// Plot more momentum indicators
plot(mfi_centered, title="26_MFI_Centered", color=color.yellow, linewidth=1)
plot(ac, title="27_AC", color=color.aqua, linewidth=1)
plot(ppo_pct, title="28_PPO_Pct", color=color.olive, linewidth=1)
plot(stoch_rsi_centered, title="29_StochRSI_Centered", color=color.silver, linewidth=1)
plot(klinger, title="30_Klinger", color=color.gray, linewidth=1)
// Plot trend continuation
plot(tii, title="31_TII", color=color.blue, linewidth=1)
plot(ichimoku_signal, title="32_Ichimoku_Signal", color=color.red, linewidth=1)
plot(mama_momentum, title="33_MAMA_Momentum", color=color.green, linewidth=1)
plot(zlema_momentum, title="34_ZLEMA_Momentum", color=color.orange, linewidth=1)
plot(bop, title="35_BOP", color=color.purple, linewidth=1)
// Plot volume continuation
plot(nvi, title="36_NVI", color=color.navy, linewidth=1)
plot(pvi, title="37_PVI", color=color.maroon, linewidth=1)
plot(momentum_pct, title="38_Momentum_Pct", color=color.teal, linewidth=1)
plot(roc, title="39_ROC", color=color.lime, linewidth=1)
plot(rvi, title="40_RVI", color=color.fuchsia, linewidth=1)
// Plot volatility continuation
plot(dc_position, title="41_DC_Position", color=color.yellow, linewidth=1)
plot(rvi_volatility, title="42_RVI_Volatility", color=color.aqua, linewidth=1)
plot(hv, title="43_Historical_Vol", color=color.olive, linewidth=1)
plot(gk_volatility, title="44_GK_Volatility", color=color.silver, linewidth=1)
plot(parkinson, title="45_Parkinson_Vol", color=color.gray, linewidth=1)
// Plot final oscillators
plot(rogers_satchell, title="46_RS_Volatility", color=color.blue, linewidth=1)
plot(elder_power, title="47_Elder_Power", color=color.red, linewidth=1)
plot(stc, title="48_STC", color=color.green, linewidth=1)
plot(coppock, title="49_Coppock", color=color.orange, linewidth=1)
plot(kst, title="50_KST", color=color.purple, linewidth=1)
// Plot final indicators
plot(ppo_histogram, title="51_PPO_Histogram", color=color.navy, linewidth=1)
plot(pvt, title="52_PVT", color=color.maroon, linewidth=1)
// |----- Reference Lines -----| //
hline(0, "Zero Line", color=color.gray, linestyle=hline.style_dashed, linewidth=1)
hline(50, "Midline", color=color.gray, linestyle=hline.style_dotted, linewidth=1)
hline(-50, "Lower Midline", color=color.gray, linestyle=hline.style_dotted, linewidth=1)
hline(25, "Upper Threshold", color=color.gray, linestyle=hline.style_dotted, linewidth=1)
hline(-25, "Lower Threshold", color=color.gray, linestyle=hline.style_dotted, linewidth=1)
// |----- Enhanced Information Table -----| //
if show_table and barstate.islast
table_position = position.top_right
table_text_size = table_size == "Tiny" ? size.tiny : table_size == "Small" ? size.small : size.normal
var table info_table = table.new(table_position, 3, 18, bgcolor=color.new(color.white, 85), border_width=1, border_color=color.gray)
// Headers
table.cell(info_table, 0, 0, 'Category', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.blue, 70))
table.cell(info_table, 1, 0, 'Indicator', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.blue, 70))
table.cell(info_table, 2, 0, 'Value', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.blue, 70))
// Key Momentum Indicators
table.cell(info_table, 0, 1, 'MOMENTUM', text_color=color.purple, text_size=table_text_size, bgcolor=color.new(color.purple, 90))
table.cell(info_table, 1, 1, 'RSI Centered', text_color=color.purple, text_size=table_text_size)
table.cell(info_table, 2, 1, str.tostring(rsi_centered, '0.00'), text_color=color.purple, text_size=table_text_size)
table.cell(info_table, 0, 2, '', text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 1, 2, 'Stoch Centered', text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 2, 2, str.tostring(stoch_centered, '0.00'), text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 0, 3, '', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 1, 3, 'Williams %R', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 2, 3, str.tostring(williams_r, '0.00'), text_color=color.red, text_size=table_text_size)
table.cell(info_table, 0, 4, '', text_color=color.orange, text_size=table_text_size)
table.cell(info_table, 1, 4, 'MACD Histogram', text_color=color.orange, text_size=table_text_size)
table.cell(info_table, 2, 4, str.tostring(macd_histogram, '0.000'), text_color=color.orange, text_size=table_text_size)
table.cell(info_table, 0, 5, '', text_color=color.green, text_size=table_text_size)
table.cell(info_table, 1, 5, 'CCI', text_color=color.green, text_size=table_text_size)
table.cell(info_table, 2, 5, str.tostring(cci, '0.00'), text_color=color.green, text_size=table_text_size)
// Key Trend Indicators
table.cell(info_table, 0, 6, 'TREND', text_color=color.navy, text_size=table_text_size, bgcolor=color.new(color.navy, 90))
table.cell(info_table, 1, 6, 'SMA Momentum %', text_color=color.navy, text_size=table_text_size)
table.cell(info_table, 2, 6, str.tostring(sma_momentum, '0.00'), text_color=color.navy, text_size=table_text_size)
table.cell(info_table, 0, 7, '', text_color=color.maroon, text_size=table_text_size)
table.cell(info_table, 1, 7, 'EMA Momentum %', text_color=color.maroon, text_size=table_text_size)
table.cell(info_table, 2, 7, str.tostring(ema_momentum, '0.00'), text_color=color.maroon, text_size=table_text_size)
table.cell(info_table, 0, 8, '', text_color=color.teal, text_size=table_text_size)
table.cell(info_table, 1, 8, 'SAR Trend', text_color=color.teal, text_size=table_text_size)
table.cell(info_table, 2, 8, str.tostring(sar_trend, '0'), text_color=color.teal, text_size=table_text_size)
table.cell(info_table, 0, 9, '', text_color=color.lime, text_size=table_text_size)
table.cell(info_table, 1, 9, 'Linear Regression', text_color=color.lime, text_size=table_text_size)
table.cell(info_table, 2, 9, str.tostring(lr_slope, '0.000'), text_color=color.lime, text_size=table_text_size)
// Key Volatility Indicators
table.cell(info_table, 0, 10, 'VOLATILITY', text_color=color.yellow, text_size=table_text_size, bgcolor=color.new(color.yellow, 90))
table.cell(info_table, 1, 10, 'ATR %', text_color=color.yellow, text_size=table_text_size)
table.cell(info_table, 2, 10, str.tostring(atr_pct, '0.00'), text_color=color.yellow, text_size=table_text_size)
table.cell(info_table, 0, 11, '', text_color=color.aqua, text_size=table_text_size)
table.cell(info_table, 1, 11, 'BB Position', text_color=color.aqua, text_size=table_text_size)
table.cell(info_table, 2, 11, str.tostring(bb_position, '0.00'), text_color=color.aqua, text_size=table_text_size)
table.cell(info_table, 0, 12, '', text_color=color.olive, text_size=table_text_size)
table.cell(info_table, 1, 12, 'KC Position', text_color=color.olive, text_size=table_text_size)
table.cell(info_table, 2, 12, str.tostring(kc_position, '0.00'), text_color=color.olive, text_size=table_text_size)
// Key Volume Indicators
table.cell(info_table, 0, 13, 'VOLUME', text_color=color.blue, text_size=table_text_size, bgcolor=color.new(color.blue, 90))
table.cell(info_table, 1, 13, 'Volume ROC', text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 2, 13, str.tostring(vroc, '0.00'), text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 0, 14, '', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 1, 14, 'EOM', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 2, 14, str.tostring(eom_sma, '0.000'), text_color=color.red, text_size=table_text_size)
// Key Oscillators
table.cell(info_table, 0, 15, 'OSCILLATORS', text_color=color.purple, text_size=table_text_size, bgcolor=color.new(color.purple, 90))
table.cell(info_table, 1, 15, 'Awesome Osc', text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 2, 15, str.tostring(ao, '0.000'), text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 0, 16, '', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 1, 16, 'Fisher Transform', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 2, 16, str.tostring(fisher, '0.000'), text_color=color.red, text_size=table_text_size)
// Summary Statistics
table.cell(info_table, 0, 17, 'SUMMARY', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.gray, 70))
table.cell(info_table, 1, 17, 'Total Indicators: 52', text_color=color.black, text_size=table_text_size)
regime_color = rsi_centered > 10 ? color.green : rsi_centered < -10 ? color.red : color.gray
regime_text = rsi_centered > 10 ? "BULLISH" : rsi_centered < -10 ? "BEARISH" : "NEUTRAL"
table.cell(info_table, 2, 17, regime_text, text_color=regime_color, text_size=table_text_size)
This makes it the perfect “indicator backbone” for quantitative and systematic traders who want to prototype, combine, and test new regime detection models—especially in combination with the Markov Chain indicator.
How to use this script with the Markov Chain for research and backtesting:
Add the Enhanced Indicator Export to your chart.
Every calculated indicator is available as an individual data stream.
Connect the indicator(s) you want as custom input(s) to the Markov Chain’s “Custom Indicators” option.
In the Markov Chain indicator’s settings, turn ON the custom indicator mode.
For each of the three custom indicator inputs, select the exported plot from the Enhanced Export script—the menu lists all 45+ signals by name.
This creates a powerful, modular regime-detection engine where you can mix-and-match momentum, trend, volume, or custom combinations for advanced filtering.
Backtest regime logic directly.
Once you’ve connected your chosen indicators, the Markov Chain script performs regime detection (Bull/Neutral/Bear) based on your selected features—not just price returns.
The regime detection is robust, automatically normalized (using Z-score), and outputs bias (1, -1, 0) for plug-and-play integration.
Export the regime bias for programmatic use.
As described above, use input.source() in your Pine Script strategy or system and link the bias output.
You can now filter signals, control trade direction/size, or design pairs-trading that respect true, indicator-driven market regimes.
With this framework, you’re not limited to static or simplistic regime filters. You can rigorously define, test, and refine what “market regime” means for your strategies—using the technical features that matter most to you.
Optimize your signal generation by backtesting across a universe of meaningful indicator blends.
Enhance risk management with objective, real-time regime boundaries.
Accelerate your research: iterate quickly, swap indicator components, and see results with minimal code changes.
Automate multi-asset or pairs-trading by integrating regime context directly into strategy logic.
Add both scripts to your chart, connect your preferred features, and start investigating your best regime-based trades—entirely within the TradingView ecosystem.
References & Further Reading
Ang, A., & Bekaert, G. (2002). “Regime Switches in Interest Rates.” Journal of Business & Economic Statistics, 20(2), 163–182.
Hamilton, J. D. (1989). “A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle.” Econometrica, 57(2), 357–384.
Markov, A. A. (1906). "Extension of the Limit Theorems of Probability Theory to a Sum of Variables Connected in a Chain." The Notes of the Imperial Academy of Sciences of St. Petersburg.
Guidolin, M., & Timmermann, A. (2007). “Asset Allocation under Multivariate Regime Switching.” Journal of Economic Dynamics and Control, 31(11), 3503–3544.
Murphy, J. J. (1999). Technical Analysis of the Financial Markets. New York Institute of Finance.
Brock, W., Lakonishok, J., & LeBaron, B. (1992). “Simple Technical Trading Rules and the Stochastic Properties of Stock Returns.” Journal of Finance, 47(5), 1731–1764.
Zucchini, W., MacDonald, I. L., & Langrock, R. (2017). Hidden Markov Models for Time Series: An Introduction Using R (2nd ed.). Chapman and Hall/CRC.
On Quantitative Finance and Markov Models:
Lo, A. W., & Hasanhodzic, J. (2009). The Heretics of Finance: Conversations with Leading Practitioners of Technical Analysis. Bloomberg Press.
Patterson, S. (2016). The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution. Penguin Press.
TradingView Pine Script Documentation: www.tradingview.com
TradingView Blog: “Use an Input From Another Indicator With Your Strategy” www.tradingview.com
GeeksforGeeks: “What is the Difference Between Markov Chains and Hidden Markov Models?” www.geeksforgeeks.org
What makes this indicator original and unique?
- On‑chart, real‑time Markov. The chain is drawn directly on your chart. You see the current regime, its tendency to stay (self‑loop), and the usual next step (arrows) as bars confirm.
- Source‑agnostic by design. The engine runs on any series you select via input.source() — price, your own oscillator, a composite score, anything you compute in the script.
- Automatic normalization + regime mapping. Different inputs live on different scales. The script standardizes your chosen source and maps it into clear regimes (e.g., Bull / Bear / Neutral) without you micromanaging thresholds each time.
- Rolling, bar‑by‑bar learning. Transition tendencies are computed from a rolling window of confirmed bars. What you see is exactly what the market did in that window.
- Fast experimentation. Switch the source, adjust the window, and the Markov view updates instantly. It’s a rapid way to test ideas and feel regime persistence/switch behavior.
Integrate your own signals (using input.source())
- In settings, choose the Source . This is powered by input.source() .
- Feed it price, an indicator you compute inside the script, or a custom composite series.
- The script will automatically normalize that series and process it through the Markov engine, mapping it to regimes and updating the on‑chart spheres/arrows in real time.
Credits:
Deep gratitude to @RicardoSantos for both the foundational Markov chain processing engine and inspiring open-source contributions, which made advanced probabilistic market modeling accessible to the TradingView community.
Special thanks to @Alien_Algorithms for the innovative and visually stunning 3D sphere logic that powers the indicator’s animated, regime-based visualization.
Disclaimer
This tool summarizes recent behavior. It is not financial advice and not a guarantee of future results.
Real Woodies CCIAs always, this is not financial advice and use at your own risk. Trading is risky and can cost you significant sums of money if you are not careful. Make sure you always have a proper entry and exit plan that includes defining your risk before you enter a trade.
Ken Wood is a semi-famous trader that grew in popularity in the 1990s and early 2000s due to the establishment of one of the earliest trading forums online. This forum grew into "Woodie's CCI Club" due to Wood's love of his modified Commodity Channel Index (CCI) that he used extensively. From what I can tell, the website is still active and still follows the same core principles it did in the early days, the CCI is used for entries, range bars are used to help trader's cut down on the noise, and the optional addition of Woodie's Pivot Points can be used as further confirmation of support and resistance. This is my take on his famous "Woodie's CCI" that has become standard on many charting packages through the years, including a TradingView sponsored version as one of the many stock indicators provided by TradingView. Woodie has updated his CCI through the years to include several very cool additions outside of the standard CCI. I will have to say, I am a bit biased, but I think this is hands down one of the best indicators I have ever used, and I am far too young to have been part of the original CCI Club. Being a daytrader primarily, this fits right in my timeframe wheel house. Woodie designed this indicator to work on a day-trading time scale and he frequently uses this to trade futures and commodity contracts on the 30 minute, often even down to the one minute timeframe. This makes it unique in that it is probably one of the only daytrading-designed indicators out there that I am aware of that was not a popular indicator, like the MACD or RSI, that was just adopted by daytraders.
The CCI was originally created by Donald Lambert in 1980. Over time, it has become an extremely popular house-hold indicator, like the Stochastics, RSI, or MACD. However, like the RSI and Stochastics, there are extensive debates on how the CCI is actually meant to be used. Some trade it like a reversal indicator, where values greater than 100 or less than -100 are considered overbought or oversold, respectively. Others trade it like a typical zero-line cross indicator, where once the value goes above or below the zero-line, a trade should be considered in that direction. Lastly, some treat it as strictly a momentum indicator, where values greater than 100 or less than -100 are seen as strong momentum moves and when these values are reached, a new strong trend is establishing in the direction of the move. The CCI itself is nothing fancy, it just visualizes the distance of the closing price away from a user-defined SMA value and plots it as a line. However, Woodie's CCI takes this simple concept and adds to it with an indicator with 5 pieces to it designed to help the trader enter into the highest probability setups. Bear with me, it initially looks super complicated, but I promise it is pretty straight-forward and a fun indicator to use.
1) The CCI Histogram. This is your standard CCI value that you would find on the normal CCI. Woodie's CCI uses a value of 14 for most trades and a value of 20 when the timeframe is equal to or greater than 30minutes. I personally use this as a 20-period CCI on all time frames, simply for the fact that the 20 SMA is a very popular moving average and I want to know what the crowd is doing. This is your coloured histogram with 4 colours. A gray colouring is for any bars above or below the zero line for 1-4 bars. A yellow bar is a "trend bar", where the long period CCI has been above/below the zero line for 5 consecutive bars, indicating that a trend in the current direction has been established. Blue bars above and red bars below are simply 6+n number of bars above or below the zero line confirming trend. These are used for the Zero-Line Reject Trade (explained below). The CCI Histogram has a matching long-period CCI line that is painted the same colour as the histogram, it is the same thing but is used just to outline the Histogram a bit better.
2) The CCI Turbo line. This is a sped-up 6 period CCI. This is to be used for the Zero-Line Reject trades, trendline breaks, and to identify shorter term overbought/oversold conditions against the main trend. This is coloured as the white line.
3) The Least Squares Moving Average Baseline (LSMA) Zero Line. You will notice that the Zero Line of the indicator is either green or red. This is based on when price is above or below the 25-period LSMA on the chart. The LSMA is a 25 period linear regression moving average and is one of the best moving averages out there because it is more immune to noise than a typical MA. Statistically, an LSMA is designed to find the line of best fit across the lookback periods and identify whether price is advancing, declining, or flat, without the whipsaw that other MAs can be privy to. The zero line of the indicator will turn green when the close candle is over the LSMA or red when it is below the LSMA. This is meant to be a confirmation tool only and the CCI Histogram and Turbo Histogram can cross this zero line without any corresponding change in the colour of the zero line on that immediate candle.
4) The +100 and -100 lines are used in two ways. First, they can be used by the CCI Histogram and CCI Turbo as a sort of minor price resistance and if the CCI values cannot get through these, it is considered weakness in that trade direction until they do so. You will notice that both of these lines are multi-coloured. They have been plotted with the ChopZone Indicator, another TradingView built-in indicator. The ChopZone is a trend identification tool that uses the slope and the direction of a 34-period EMA to identify when price is trending or range bound. While there are ~10 different colours, the main two a trader needs to pay attention to are the turquoise/cyan blue, which indicates price is in an uptrend, and dark red, which indicates price is in a downtrend based on the slope and direction of the 34 EMA. All other colours indicate "chop". These colours are used solely for the Zero-Line Reject and pattern trades discussed below. They are plotted both above and below so you can easily see the colouring no matter what side of the zero line the CCI is on.
5) The +200 and -200 lines are also used in two ways. First, they are considered overbought/oversold levels where if price exceeds these lines then it has moved an extreme amount away from the average and is likely to experience a pullback shortly. This is more useful for the CCI Histogram than the Turbo CCI, in all honesty. You will also notice that these are coloured either red, green, or yellow. This is the Sidewinder indicator portion. The documentation on this is extremely sparse, only pointing to a "relationship between the LSMA and the 34 EMA" (see here: tlc.thinkorswim.com). Since I am not a member of Woodie's CCI Club and never intend to be I took some liberty here and decided that the most likely relationship here was the slope of both moving averages. Therefore, the Sidewinder will be green when both the LSMA and the 34 EMA are rising, red when both are falling, and yellow when they are not in agreement with one another (i.e. one rising/flat while the other is flat/falling). I am a big fan of Dr. Alexander Elder as those who follow me know, so consider this like Woodie's version of the Elder Impulse System. I will fully admit that this version of the Sidewinder is a guess and may not represent the real Sidewinder indicator, but it is next to impossible to find any information on this, so I apologize, but my version does do something useful anyways. This is also to be used only with the Zero-Line Reject trades. They are plotted both above and below so you can easily see the colouring no matter what side of the zero line the CCI is on.
How to Trade It According to Woodie's CCI Club:
Now that I have all of my components and history out of the way, this is what you all care about. I will only provide a brief overview of the trades in this system, but there are quite a few more detailed descriptions listed in the Woodie's CCI Club pamphlet. I have had little success trading the "patterns" but they do exist and do work on occasion. I just prefer to trade with the flow of the markets rather than getting overly scalpy. If you are interested in these patterns, see the pamphlet here (www.trading-attitude.com), hop into the forums and see for yourself, or check out a couple of the YouTube videos.
1) Zero line cross. As simple as any other momentum oscillator out there. When the long period CCI crosses above or below the zero line open a trade in that direction. Extra confirmation can be had when the CCI Turbo has already broken the +100/-100 line "resistance or support". Trend traders may wish to wait until the yellow "trend confirmation bar" has been printed.
2) Zero Line Reject. This is when the CCI Turbo heads back down to the zero line and then bounces back in the same direction of the prevailing trend. These are fantastic continuation trades if you missed the initial entry either on the zero line cross or on the trend bar establishment. ZLR trades are only viable when you have the ChopZone indicator showing a trend (turquoise/cyan for uptrend, dark red for downtrend), the LSMA line is green for an uptrend or red for a downtrend, and the SideWinder is either green confirming the uptrend or red confirming the downtrend.
3) Hook From Extreme. This is the exact same as the Zero Line Reject trade, however, the CCI Turbo now goes to the +100/-100 line (whichever is opposite the currently established trend) and then hooks back into the established trend direction. Ideally the HFE trade needs to have the Long CCI Histogram above/below the corresponding 100 level and the CCI Turbo both breaks the 100 level on the trend side and when it does break it has increased ~20 points from the previous value (i.e. CCI Histogram = +150 with LSMA, CZ, and SW all matching up and trend bars printed on CCI Histogram, CCI Turbo went to -120 and bounced to +80 on last 2 bars, current bar closes with CCI Turbo closing at +110).
4) Trend Line Break. Either the CCI Turbo or CCI Histogram, whichever you prefer (I find the Turbo a bit more accurate since its a faster value) creates a series of higher highs/lows you can draw a trend line linking them. When the line breaks the trendline that is your signal to take a counter trade position. For example, if the CCI Turbo is making consistently higher lows and then breaks the trendline through the zero line, you can then go short. This is a good continuation trade.
5) The Tony Trade. Consider this like a combination zero line reject, trend line break, and weak zero line cross all in one. The idea is that the SW, CZ, and LSMA values are all established in one direction. The CCI Histogram should be in an established trend and then cross the zero line but never break the 100 level on the new side as long as it has not printed more than 9 bars on the new side. If the CCI Histogram prints 9 or less bars on the new side and then breaks the trendline and crosses back to the original trend side, that is your signal to take a reversal trade. This is best used in the Elder Triple Screen method (discussed in final section) as a failed dip or rip.
6) The GB100 Trade. This is a similar trade as the Tony Trade, however, the CCI Histogram can break the 100 level on the new side but has to have made less than 6 bars on the new side. A trendline break is not necessary here either, it is more of a "pop and drop" or "momentum failure" trade trying in the new direction.
7) The Famir Trade. This is a failed CCI Long Histogram ZLR trade and is quite complicated. I have never traded this but it is in the pamphlet. Essentially you have a typical ZLR reject (i.e. all components saying it is likely a long/short continuation trade), but the ZLR only stays around the 50 level, goes back to the trend side, fails there as well immediately after 1 bar and then rebreaks to the new side. This is important to be considered with the LSMA value matching the side of the trade, so if the Famir says to go long, you need the LSMA indicator to also say to go long.
8) The Vegas Trade. This is essentially a trend-reversal trade that takes into account the LSMA and a cup and handle formation on the CCI Long Histogram after it has reached an extreme value (+200/-200). You will see the CCI Histogram hit the extreme value, head towards the zero line, and then sort of round out back in the direction of the extreme price. The low point where it reversed back in the direction of the extreme can be considered support or resistance on the CCI and once the CCI Long Histogram breaks this level again, with LSMA confirmation, you can take a counter trend trade with a stop under/over the highest/lowest point of the last 2 bars as you want to be out quickly if you are wrong without much damage but can get a huge win if you are right and add later to the position once a new trade has formed.
9) The Ghost Trade. This is nothing more than a(n) (inverse) head and shoulders pattern created on the CCI. Draw a trend line connecting the head and shoulders and trade a reversal trade once the CCI Long Histogram breaks the trend line. Same deal as the Vegas Trade, stop over/under the most recent 2 bar high/low and add later if it is a winner but cut quickly if it is a loser.
Like I said, this is a complicated system and could quite literally take years to master if you wanted to go into the patterns and master them. I prefer to trade it in a much simpler format, using the Elder Triple Screen System. First, since I am a day trader, I look to use the 20 period Woodie's on the hourly and look at the CZ, SW, and LSMA values to make sure they all match the direction of the CCI Long Histogram (a trend establishment is not necessary here). It shows you the hourly trend as your "tide". I then drill down to the 15 minute time frame and use the Turbo CCI break in the opposite direction of the trend as my "wave" and to indicate when there is a dip or rip against the main trend. Lastly, I drill down to a 3 minute time frame and enter when the CCI Long Histogram turns back to match the main trend ("ripple") as long as the CCI Turbo has broken the 100 level in the matched direction.
Enjoy, and please read the pamphlet if you have any questions about the patterns as they are not how I use these and will not be able to answer those questions.
Relative Strength Scoring SystemRelative Strength Scoring System :
Important prerequisite :
This indicator can be loaded on any forex chart, i.e. a currency pair, but must not be loaded on any other asset due to certain market closures.
The chart timeframe must be less than or equal to the trading timeframe, which is the indicator's first parameter. A timeframe equal to that of the "Trading Timeframe" parameter is preferable.
Introduction :
This indicator measures the relative strength of a currency against all other currencies using spread formulas. It gives an indication of which currencies are bullish, neutral or bearish. The ultimate aim of this indicator is to find out which pair will generate a higher probability of gain than the others by pairing the most bullish pair with the most bearish pair.
Spread formulas :
To find the relative strength of a currency compared with others, we use the following spreads formulas :
USD = (FX:USDJPY/100+SAXO:USDEUR+FX:USDCHF+SAXO:USDGBP+FX:USDCAD+SAXO:USDAUD+FX_IDC:USDNZD)/7
JPY = (SAXO:JPYUSD/100+FX_IDC:JPYAUD/100+FX_IDC:JPYCAD/100+FX_IDC:JPYNZD/100+FX_IDC:JPYCHF/100+SAXO:JPYEUR/100+FX_IDC:JPYGBP/100)/7
CHF = (FX:CHFJPY/100+SAXO:CHFUSD+SAXO:CHFEUR+FX_IDC:CHFGBP+FX_IDC:CHFCAD+SAXO:CHFAUD+FX_IDC:CHFNZD)/7
EUR = (FX:EURJPY/100+FX:EURUSD+FX:EURCHF+FX:EURGBP+FX:EURCAD+FX:EURAUD+FX:EURNZD)/7
GBP = (FX:GBPJPY/100+FX:GBPUSD+FX:GBPCHF+SAXO:GBPEUR+FX:GBPCAD+FX:GBPAUD+FX:GBPNZD)/7
CAD = (FX:CADJPY/100+SAXO:CADUSD+FX:CADCHF+FX_IDC:CADGBP+SAXO:CADEUR+FX_IDC:CADAUD+FX_IDC:CADNZD)/7
AUD = (FX:AUDJPY/100+FX:AUDUSD+FX:AUDCHF+SAXO:AUDGBP+FX:AUDCAD+SAXO:AUDEUR+FX:AUDNZD)/7
NZD = (FX:NZDJPY/100+FX:NZDUSD+FX:NZDCHF+SAXO:NZDGBP+FX:NZDCAD+SAXO:NZDAUD+SAXO:NZDEUR)/7
CRYPTO = (BITSTAMP:BTCUSD+BITSTAMP:ETHUSD+BITSTAMP:LTCUSD+BITSTAMP:BCHUSD)/4
Timeframes :
As mentioned in the prerequisites, the chart timeframe must not be greater than the trading timeframe. The latter corresponds to the timeframe chosen by the trader to enter a position, and is the indicator's first parameter. Once this has been chosen, the algorithm selects the timeframes of the "Trend" and "Velocity" charts. Here's how it allocates them :
Trading TF => ("Velocity TF", "Trend TF")
"5min" => ("15min ", "60min")
"15min" => ("60min ", "4h")
"30min" => ("2h ", "8h")
"60min" => ("4h ", "12h")
"4h" => ("12h", "1D")
"6h" => ("1D", "3D")
"8h" => ("1D", "4D")
"12h" => ("2D", "1W")
"1D" => ("3D", "1W")
Trend Scoring System :
When the timeframe of the trend graph has been allocated, the algorithm will establish this graph's score using three criteria :
Trend chart pivot points: if the last two pivots, high and low, are increasing, the score is 1; if they are decreasing, the score is -1; else the score is 0.
SMA: if its slope is increasing with a candle strictly above the SMA value, the score is 1; if its slope is decreasing with a candle strictly below it, the score is -1; otherwise, it is 0.
MACD: if the MACD is positive, the score is 1, if it is negative, the score is -1; else it's 0.
We then sum the scores of these three criteria to find the trend score.
Velocity Scoring System :
In the same way, we analyze the score of the "velocity" graph with its corresponding timeframe using three criteria :
The EMA: if its slope is increasing with a candle strictly above the EMA value, the score is 1; if its slope is decreasing with a candle strictly below it, the score is -1; otherwise, it is 0.
The RSI: if the RSI's EMA has an increasing slope with an RSI strictly greater than the value of this EMA, the score is 1; and if the RSI's EMA has a decreasing slope with an RSI strictly less than this EMA, the score is -1; otherwise it is 0.
SAR parabolic: if the SAR is below the price, the score is 1; if it is above the price, the score is -1.
We then sum the scores of these three criteria to find the velocity score.
Relative Strength Scoring System :
Once the trend score and velocity score have been calculated, we determine the relative strength score of each currency using the following algorithm :
If trend score >=2 and velocity score >=2, the currency is bullish.
If trend score <=2 and velocity score <=2, currency is bearish
If (trendScore>=2 or velocityScore>=2) and (trendScore=1 or velocityScore=1) the currency is not yet bullish
If (trendScore<=2 or velocityScore<=2) and (trendScore=-1 or velocityScore=-1) the currency is not yet bearish.
Otherwise the currency is neutral
Parameters :
Trading Timeframe: the trading timeframe chosen by the trader for which he makes his position entry and exit decisions. Default is 1h
Pivot Legs: Parameter used for the chart "Trend" setting the pivot strength to the right and left of high/low. Default is 2
SMA Length: SMA length of the chart "Trend". Default is 20
MACD Fast Length: Length of the MACD fast SMA calculated on the chart "Trend". Default is 12
MACD Slow Length: Length of the MACD slow SMA calculated on the chart "Trend". Default is 26
MACD Signal Length: Length of the MACD signal SMA calculated on the chart "Trend". Default is 9
EMA Length: EMA length of the "Velocity" graph. Default is 13
RSI Length: RSI length of the "Velocity" graph. Default is 14
RSI EMA Length: Length of the RSI EMA. Default is 9
Parabolic SAR Start: Start of the SAR parabola in the "Velocity" graph. Default is 0.02
Parabolic SAR Increment: Increment of the SAR parabola in the "Velocity" graph. Default is 0.02
Parabolic SAR Max: Maximum of the SAR parabola in the "Velocity" graph. Default is 0.2
Conclusion :
This indicator has been designed to determine the relative strength of the major currencies against each other. The aim is to know which pair to trade at the right time in order to maximize the probability of a successful trade. For example, if the USD is bullish and the NZD bearish, we'll short the NZDUSD pair.
Enjoy this indicator and don't forget to take the trade ;)
RRG Relative Strength# RRG Relative Strength (RRG RS)
Compare any symbol to a benchmark using two RRG-style lines: **RS-Ratio** (trend of relative strength) and **RS-Momentum** (momentum of that trend). Both are centered at **100**:
- **RS-Ratio > 100** → outperforming the benchmark
- **RS-Ratio < 100** → underperforming
- **RS-Momentum** often **leads** RS-Ratio (crosses 100 earlier)
# How it works
1) Relative Strength (RS): RS = Close(symbol) / Close(benchmark)
2) Normalize around 100: smooth RS with EMA and divide RS by that EMA
3) RS-Ratio: EMA( RS / EMA(RS, Length), LenSmooth ) * 100
4) RS-Momentum: RS-Ratio / EMA(RS-Ratio, LenSmooth) * 100
# Inputs
- Length (default 14): normalization window for RS
- Length Smooth (default 20): smoothing window for RS-Ratio & RS-Momentum
# Benchmark (auto)
- US: SP:SPX (S&P 500)
- Vietnam: HOSE:VNINDEX
- Crypto: INDEX:BTCUSD
(Modify the mapping if needed, or replace with your own input.symbol().)
# How to read
- Improving: RS-Momentum crosses above 100 while RS-Ratio turns up
- Leading: RS-Ratio > 100 with RS-Momentum ≥ 100
- Weakening: RS-Momentum drops below 100; RS-Ratio often follows
# Timeframes & presets
- Works on Daily and Weekly charts
- Daily (fast): 14 / 20
- Approx. weekly behavior on Daily: 50 / 60
Note: Values usually hover near 100 (e.g., ~90–110) but are not strictly bounded. Ensure your symbol and benchmark trade in comparable sessions/currencies.
Fibs Has Lied 🌟 Fibs Has Lied - Indicator Overview 🌟
Designed for indices like US30, NQ, and SPX, this indicator highlights setups where price interacts with key EMA levels during specific trading sessions (default: 6:30–11:30 AM EST).
🌟 Key Features & Levels 🌟
🔹EMA Crossover Setups
The indicator uses the 100-period and 200-period EMAs to identify bullish and bearish setups:
- Bullish Setup: Triggers when the 100 EMA crosses above the 200 EMA, followed by two consecutive candles opening above the 100 EMA, with the low within a specified point distance (e.g., 20 points for US30).
- Bearish Setup: Triggers when the 100 EMA crosses below the 200 EMA, followed by two consecutive candles opening below the 100 EMA, with the high within the point distance.
- Signals are marked with green (buy) or red (sell) triangles and text, ensuring you don’t miss a setup. 📈
🔹 Reset Conditions for Re-Entries
After an initial setup, the indicator watches for “reset” opportunities:
- Buy Reset: If price moves below the 200 EMA after a bullish crossover, then returns with two consecutive candles where lows are above the 100 EMA (within point distance), a new buy signal is plotted.
- Sell Reset: If price moves above the 200 EMA after a bearish crossover, then returns with two consecutive candles where highs are below the 100 EMA (within point distance), a new sell signal is plotted.
This feature captures additional entries after liquidity grabs or fakeouts, aligning with ICT’s manipulation concepts. 🔄
🔹 Session-Based Filtering
Focus your trades during high-liquidity windows! The default session (6:30–11:30 AM EST, New York timezone) targets the London/NY overlap, where price often seeks liquidity or sets up for reversals. Toggle the time filter off for 24/7 signals if desired. 🕒
🔹Symbol-Specific Point Distance
Customizable entry zones based on your chosen index:
- US30: 20 points from the 100 EMA.
- NQ: 3 points from the 100 EMA.
- SPX: 2.5 points from the 100 EMA.
This ensures setups are tailored to the volatility of your market, maximizing relevance. 🎯
🔹 Market Structure Markers (Optional)
Visualize swing points with pivot-based labels:
- HH (Higher High): Signals uptrend continuation.
- HL (Higher Low): Indicates potential bullish support.
- LH (Lower High): Suggests weakening uptrend or reversal.
- LL (Lower Low): Points to downtrend continuation.
- Toggle these on/off to keep your chart clean while analyzing trend direction. 📊
🔹 EMA Visualization
Optionally plot the 100 EMA (blue) and 200 EMA (red) to see key levels where price reacts. These act as dynamic support/resistance, perfect for spotting liquidity pools or ICT’s Power of 3 setups. ⚖️
🌟 Customization Options 🌟
- Symbol Selection: Choose US30, NQ, or SPX to adjust point distance for entries.
- Time Filter: Enable/disable the 6:30–11:30 AM EST session to focus on high-liquidity periods.
- EMA Display: Toggle 100/200 EMAs on/off to reduce chart clutter.
- Market Structure: Show/hide HH/HL/LH/LL labels for cleaner analysis.
- Signal Markers: Green (buy) and red (sell) triangles with text are auto-plotted for easy identification.
🌟 Usage Tips 🌟
- Best Timeframes: Use on 3m for intraday scalping and 30m for swing trades.
- Combine with ICT Tools: Pair with order blocks, fair value gaps, or kill zones for stronger setups.
- Focus on Session: The default 6:30–11:30 AM EST session captures London/NY volatility—perfect for liquidity-driven moves.
- Avoid Overcrowding: Disable market structure or EMAs if you only want setup signals.
CCO_LibraryLibrary "CCO_Library"
Contrarian Crowd Oscillator (CCO) Library - Multi-oscillator consensus indicator for contrarian trading signals
@author B3AR_Trades
calculate_oscillators(rsi_length, stoch_length, cci_length, williams_length, roc_length, mfi_length, percentile_lookback, use_rsi, use_stochastic, use_williams, use_cci, use_roc, use_mfi)
Calculate normalized oscillator values
Parameters:
rsi_length (simple int) : (int) RSI calculation period
stoch_length (int) : (int) Stochastic calculation period
cci_length (int) : (int) CCI calculation period
williams_length (int) : (int) Williams %R calculation period
roc_length (int) : (int) ROC calculation period
mfi_length (int) : (int) MFI calculation period
percentile_lookback (int) : (int) Lookback period for CCI/ROC percentile ranking
use_rsi (bool) : (bool) Include RSI in calculations
use_stochastic (bool) : (bool) Include Stochastic in calculations
use_williams (bool) : (bool) Include Williams %R in calculations
use_cci (bool) : (bool) Include CCI in calculations
use_roc (bool) : (bool) Include ROC in calculations
use_mfi (bool) : (bool) Include MFI in calculations
Returns: (OscillatorValues) Normalized oscillator values
calculate_consensus_score(oscillators, use_rsi, use_stochastic, use_williams, use_cci, use_roc, use_mfi, weight_by_reliability, consensus_smoothing)
Calculate weighted consensus score
Parameters:
oscillators (OscillatorValues) : (OscillatorValues) Individual oscillator values
use_rsi (bool) : (bool) Include RSI in consensus
use_stochastic (bool) : (bool) Include Stochastic in consensus
use_williams (bool) : (bool) Include Williams %R in consensus
use_cci (bool) : (bool) Include CCI in consensus
use_roc (bool) : (bool) Include ROC in consensus
use_mfi (bool) : (bool) Include MFI in consensus
weight_by_reliability (bool) : (bool) Apply reliability-based weights
consensus_smoothing (int) : (int) Smoothing period for consensus
Returns: (float) Weighted consensus score (0-100)
calculate_consensus_strength(oscillators, consensus_score, use_rsi, use_stochastic, use_williams, use_cci, use_roc, use_mfi)
Calculate consensus strength (agreement between oscillators)
Parameters:
oscillators (OscillatorValues) : (OscillatorValues) Individual oscillator values
consensus_score (float) : (float) Current consensus score
use_rsi (bool) : (bool) Include RSI in strength calculation
use_stochastic (bool) : (bool) Include Stochastic in strength calculation
use_williams (bool) : (bool) Include Williams %R in strength calculation
use_cci (bool) : (bool) Include CCI in strength calculation
use_roc (bool) : (bool) Include ROC in strength calculation
use_mfi (bool) : (bool) Include MFI in strength calculation
Returns: (float) Consensus strength (0-100)
classify_regime(consensus_score)
Classify consensus regime
Parameters:
consensus_score (float) : (float) Current consensus score
Returns: (ConsensusRegime) Regime classification
detect_signals(consensus_score, consensus_strength, consensus_momentum, regime)
Detect trading signals
Parameters:
consensus_score (float) : (float) Current consensus score
consensus_strength (float) : (float) Current consensus strength
consensus_momentum (float) : (float) Consensus momentum
regime (ConsensusRegime) : (ConsensusRegime) Current regime classification
Returns: (TradingSignals) Trading signal conditions
calculate_cco(rsi_length, stoch_length, cci_length, williams_length, roc_length, mfi_length, consensus_smoothing, percentile_lookback, use_rsi, use_stochastic, use_williams, use_cci, use_roc, use_mfi, weight_by_reliability, detect_momentum)
Calculate complete CCO analysis
Parameters:
rsi_length (simple int) : (int) RSI calculation period
stoch_length (int) : (int) Stochastic calculation period
cci_length (int) : (int) CCI calculation period
williams_length (int) : (int) Williams %R calculation period
roc_length (int) : (int) ROC calculation period
mfi_length (int) : (int) MFI calculation period
consensus_smoothing (int) : (int) Consensus smoothing period
percentile_lookback (int) : (int) Percentile ranking lookback
use_rsi (bool) : (bool) Include RSI
use_stochastic (bool) : (bool) Include Stochastic
use_williams (bool) : (bool) Include Williams %R
use_cci (bool) : (bool) Include CCI
use_roc (bool) : (bool) Include ROC
use_mfi (bool) : (bool) Include MFI
weight_by_reliability (bool) : (bool) Apply reliability weights
detect_momentum (bool) : (bool) Calculate momentum and acceleration
Returns: (CCOResult) Complete CCO analysis results
calculate_cco_default()
Calculate CCO with default parameters
Returns: (CCOResult) CCO result with standard settings
cco_consensus_score()
Get just the consensus score with default parameters
Returns: (float) Consensus score (0-100)
cco_consensus_strength()
Get just the consensus strength with default parameters
Returns: (float) Consensus strength (0-100)
is_panic_bottom()
Check if in panic bottom condition
Returns: (bool) True if panic bottom signal active
is_euphoric_top()
Check if in euphoric top condition
Returns: (bool) True if euphoric top signal active
bullish_consensus_reversal()
Check for bullish consensus reversal
Returns: (bool) True if bullish reversal detected
bearish_consensus_reversal()
Check for bearish consensus reversal
Returns: (bool) True if bearish reversal detected
bearish_divergence()
Check for bearish divergence
Returns: (bool) True if bearish divergence detected
bullish_divergence()
Check for bullish divergence
Returns: (bool) True if bullish divergence detected
get_regime_name()
Get current regime name
Returns: (string) Current consensus regime name
get_contrarian_signal()
Get contrarian signal
Returns: (string) Current contrarian trading signal
get_position_multiplier()
Get position size multiplier
Returns: (float) Recommended position sizing multiplier
OscillatorValues
Individual oscillator values
Fields:
rsi (series float) : RSI value (0-100)
stochastic (series float) : Stochastic value (0-100)
williams (series float) : Williams %R value (0-100, normalized)
cci (series float) : CCI percentile value (0-100)
roc (series float) : ROC percentile value (0-100)
mfi (series float) : Money Flow Index value (0-100)
ConsensusRegime
Consensus regime classification
Fields:
extreme_bearish (series bool) : Extreme bearish consensus (<= 20)
moderate_bearish (series bool) : Moderate bearish consensus (20-40)
mixed (series bool) : Mixed consensus (40-60)
moderate_bullish (series bool) : Moderate bullish consensus (60-80)
extreme_bullish (series bool) : Extreme bullish consensus (>= 80)
regime_name (series string) : Text description of current regime
contrarian_signal (series string) : Contrarian trading signal
TradingSignals
Trading signals
Fields:
panic_bottom_signal (series bool) : Extreme bearish consensus with high strength
euphoric_top_signal (series bool) : Extreme bullish consensus with high strength
consensus_reversal_bullish (series bool) : Bullish consensus reversal
consensus_reversal_bearish (series bool) : Bearish consensus reversal
bearish_divergence (series bool) : Bearish price-consensus divergence
bullish_divergence (series bool) : Bullish price-consensus divergence
strong_consensus (series bool) : High consensus strength signal
CCOResult
Complete CCO calculation results
Fields:
consensus_score (series float) : Main consensus score (0-100)
consensus_strength (series float) : Consensus strength (0-100)
consensus_momentum (series float) : Rate of consensus change
consensus_acceleration (series float) : Rate of momentum change
oscillators (OscillatorValues) : Individual oscillator values
regime (ConsensusRegime) : Regime classification
signals (TradingSignals) : Trading signals
position_multiplier (series float) : Recommended position sizing multiplier
Precision Trade Zone By KittisakThis indicator is designed for Money Management calculations, helping to facilitate risk management in trading, determining suitable leverage based on acceptable risk, and adjusting the Stop Loss level to align with the calculated leverage.
Abbreviation Descriptions
LR : Suitable Leverage.
EP : Entry Price.
BEP : Break-Even Point (a point where you can move your Stop Loss to prevent losses once the price reaches a certain level).
SL : Stop Loss (a recalculated Stop Loss level to match the leverage. You should use this as the Stop Loss price instead of the initial level you set).
TP : Take Profit (a point where you take profit based on the defined risk-reward ratio).
Note
When first activating the indicator, an error may occur, and no output will be displayed. This happens because you must first specify the Entry Price and Stop Loss in the indicator settings.
How Much Leverage Should You Use?
It may seem like a simple question but is difficult to answer.
Method for Calculating Suitable Leverage
Use the formula:
Leverage = Acceptable Loss / (Distance between Entry Price and Stop Loss + (Buy Fee + Sell Fee))
Calculating the Correct Stop Loss Point
(Stop Loss levels will be slightly adjusted or extended)
For Long Positions :
New Stop Loss = Entry Price * (1 - Acceptable Loss / (Calculated Leverage * 100))
For Short Positions :
New Stop Loss = Entry Price * (1 + Acceptable Loss / (Calculated Leverage * 100))
Calculating the Correct Take Profit Point
(Take Profit levels will be slightly adjusted or extended)
For Long Positions :
Take Profit = Entry Price * (1 + (Acceptable Loss / (Calculated Leverage * 100) * RR) + ((Buy Fee + Sell Fee) / 100))
For Short Positions :
Take Profit = Entry Price * (1 - (Acceptable Loss / (Calculated Leverage * 100) * RR) + ((Buy Fee + Sell Fee) / 100))
Benefits of This Calculation
1. Accurate Risk Assessment
The calculated leverage accounts for trading fees. For example, if you aim for a 2% loss, this method ensures the actual loss is exactly 2%, not more (e.g., 2% plus fees).
2. Eliminates Guesswork
Randomly setting leverage can lead to risks because the Stop Loss level may not align with your position. This calculation ensures that the leverage aligns precisely with your desired Stop Loss level.
3. Realistic Profit Targets
For example, with a 2% acceptable loss and a 1:2 RR, you expect a 4% profit. However, without this calculation, fees may reduce your profit below 4%. This method includes fees, ensuring your profit matches the intended target.
Caution
This indicator does not account for slippage or requotes. Use it with caution and allow a buffer for slippage in your calculations.
Indicator นี้มีไว้สำหรับคำนวณ Money Management ซึ่งจะช่วยอำนวยความสะดวกในการจัดการความเสี่ยงในการเทรด การคำนวณ Leverage ที่เหมาะสมกับความเสี่ยงที่คุณยอมรับได้ และจัดการจุด Stop Loss ให้เหมาะสมกับ Leverage นั้น
คำอธิบายเกี่ยวกับคำย่อ
LR หมายถึง Leverage ที่เหมาะสม
EP หมายถึง Entry Price หรือราคาเข้าซื้อ
BEP หมายถึง Break-Even Point หรือจุดคุ้มทุน (คุณสามารถย้าย Stop Loss มาที่จุดนี้เมื่อราคาไปถึงจุดหนึ่งเพื่อป้องกันการขาดทุนได้)
SL หมายถึง Stop Loss (ซึ่งเป็น Stop Loss ที่คำนวณใหม่เพื่อให้ตำแหน่งเหมาะสมกับ Leverage ที่คำนวณได้ คุณควรใช้จุดนี้เพื่อเป็นราคา Stop Loss แทนจุด Stop Loss ที่คุณกำหนดไว้ในตอนแรก)
TP หมายถึง Take Profit (เป็นจุดที่คุณจะขายทำกำไรตาม RR ที่กำหนดไว้)
* หมายเหตุ เมื่อเริ่มเปิด Indicator จะเกิด Error ขึ้น และไม่มีผลลัพท์ใด ๆ แสดงให้เห็น นั่นเป็นเพราะคุณต้องเข้าไปกำหนด Entry Price และ Stop Loss ในการตั้งค่าของ Indicator เสียก่อน
ต้องใช้ Leverage เท่าไหร่? มันเป็นคำถามที่ดูเหมือนง่าย แต่ตอบยาก
วิธีคำนวณ Leverage ที่เหมาะสม ใช้สมการคือ
Levarage = การขาดทุนที่ยอมรับได้ / (ระยะห่างระหว่าง Entry Price และ Stop Loss + (ค่าธรรมเนียมซื้อ + ค่าธรรมเนียมขาย))
นำผลลัพท์ Leverage ที่ได้มาคำนวณเพื่อหาจุด Stop Loss ที่ถูกต้อง (จุดของ Stop Loss จะมีการยืดขยายออกไปเล็กน้อย) โดยใช้สมการ
ตำแหน่ง Stop Loss ใหม่ = Entry Price * (1 - การขาดทุนที่ยอมรับได้ / (Leverage ที่คำนวณได้ * 100)) // สำหรับ Long
ตำแหน่ง Stop Loss ใหม่ = Entry Price * (1 + การขาดทุนที่ยอมรับได้ / (Leverage ที่คำนวณได้ * 100)) // สำหรับ Short
นำผลลัพท์ Leverage ที่ได้มาคำนวณเพื่อหาจุด Take Profit ที่ถูกต้อง (จุดของ Take Profit จะมีการยืดขยายออกไปเล็กน้อย) โดยใช้สมการ
ตำแหน่ง Take Profit = Entry Price * (1 + (การขาดทุนที่ยอมรับได้ / (Leverage ที่คำนวณได้ * 100) * RR) + ((ค่าธรรมเนียมซื้อ + ค่าธรรมเนียมขาย) / 100)) // สำหรับ Long
ตำแหน่ง Take Profit = Entry Price * (1 - (การขาดทุนที่ยอมรับได้ / (Leverage ที่คำนวณได้ * 100) * RR) + ((ค่าธรรมเนียมซื้อ + ค่าธรรมเนียมขาย) / 100)) // สำหรับ Short
ข้อดีของการคำนวณคือ
1. คุณจะได้ค่า Leverage ที่เหมาะสมกับความเสี่ยงที่คุณยอมรับได้โดยรวมค่าธรรมเนียมเข้าไปในนั้นแล้ว นั่นหมายความว่า ความสูญเสียจะเป็น 2% (ตามตัวอย่าง) จริง ๆ ไม่ใช่ 2% และถูกหักค่าธรรมเนียมเพิ่มอีก กลายเป็นสูญเสียมากกว่า 2%
2. การตั้ง Leverage มั่ว ๆ กลายเป็นความเสี่ยง นั่นเพราะตำแหน่งของ Stop Loss ไม่ได้อยู่ในจุดที่ควรจะเป็น การคำนวณนี้ช่วยให้คุณได้ Leverage ในตำแหน่ง Stop Loss ที่คุณต้องการโดยแท้จริง
3. ผลกำไรที่ได้รับตรงกับความต้องการจริง ๆ เช่น การขาดทุนที่ยอมรับได้ 2% และ RR 1:2 สิ่งที่คุณคิดคือกำไร 4% แต่จริง ๆ แล้วไม่ถึง 4% นั่นเพราะว่าโดนหักค่าธรรมเนียมไปส่วนหนึ่ง การคำนวณนี้ได้รวมค่าธรรมเนียมให้แล้ว คุณจึงได้กำไรที่ 4% อย่างถูกต้องตามต้องการ
ข้อควรระวัง
Indicator นี้ไม่ได้มีการควบคุมความเสี่ยงในเรื่องของ slippage หรือ requote โปรดใช้งานอย่างระมัดระวังและมีการเผื่อระยะสำหรับ slippage ด้วย
Swing EMAWhat is Swing EMA?
Swing EMA is an exponential moving average crossover-based indicator used for low-risk directional trading.
it's used for different types of Ema 20,50,100 and 200, 3 of them are plotted on chat 20,100,200.
100 and 200 Ema is used for showing support and resistance and it contains highlights area between them and its change color according to market crossover condition.
20 moving average is used for knowing Market Behaviour and changing its color according to crossover conditions of 50 and 20 Ema.
How does it work?
It contains 4 different types of moving averages 20,50,100, 200 out of 3 are plotted on the chart.
20 Ema is used for knowing current market behavior. Its changes its color based on the crossover of 50 Ema and 20 Ema, if 20 Ema is higher than 50 Ema then it changes its color to green, and its opposites are changed their color to red when 20 Ema is lower than 50 Ema.
100 and 200 Ema used as a support and resistance and is also contain highlighted areas between them its change their color based on the crossover if 100 Ema is higher than 200 Ema a then both of them are going to change color to Green and as an opposite, if 200 Ema is higher then 100 Ema is going to change its color to red.
So in simple word 100 and 200 Ema is used as support and resistance zone and 20 Ema is used to know current market behavior.
How to use it?
It is very easy to understand by looking at the example I gave where are the two different types of phrases. phrase bull phrase and bear phrase so 100 and 200 Ema is used as a support and resistance and to tell you which phrase is currently on the market on example there is a bull phrase on the left side and bear phrase on the right side by using your technical analysis you can find out a really good spot to buy your stocks on a bull phrase and too short on the bear phrase. 20 Ema is used as a knowing the current market behavior it doesn't make any difference on buying or selling as much as 100 Ema and 200 Ema.
Tips
Don't trade against the market.
Try trade on trending stocks rather than sideways stock.
The higher the area between 100 Ema and 200 Ema is the stronger the phrase.
Do Backtesting before real trading.
Enjoy Trading.
BossHouse - CCI ExtendedBossHouse - CCI Extended ( An Extended version of the Original CCI ).
The commodity channel index (CCI) is an oscillator originally introduced by Donald Lambert in 1980.
Guideline
________
Lambert's trading guidelines for the CCI focused on movements above +100 and below −100 to generate buy and sell signals. Because about 70 to 80 percent of the CCI values are between +100 and −100, a buy or sell signal will be in force only 20 to 30 percent of the time. When the CCI moves above +100, a security is considered to be entering into a strong uptrend and a buy signal is given. The position should be closed when the CCI moves back below +100. When the CCI moves below −100, the security is considered to be in a strong downtrend and a sell signal is given. The position should be closed when the CCI moves back above −100.
Since Lambert's original guidelines, traders have also found the CCI valuable for identifying reversals. The CCI is a versatile indicator capable of producing a wide array of buy and sell signals.
CCI can be used to identify overbought and oversold levels. A security would be deemed oversold when the CCI dips below −100 and overbought when it exceeds +100. From oversold levels, a buy signal might be given when the CCI moves back above −100. From overbought levels, a sell signal might be given when the CCI moved back below +100.
As with most oscillators, divergences can also be applied to increase the robustness of signals. A positive divergence below −100 would increase the robustness of a signal based on a move back above −100. A negative divergence above +100 would increase the robustness of a signal based on a move back below +100.
Trend line breaks can be used to generate signals. Trend lines can be drawn connecting the peaks and troughs. From oversold levels, an advance above −100 and trend line breakout could be considered bullish. From overbought levels, a decline below +100 and a trend line break could be considered bearish.
Settings
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Show 0 line
Lenght
Source
Any help and suggestions will be appreciated.
Marcos Issler @ Isslerman
marcos@bosshouse.com.br
Guitar Hero [theUltimator5]The Guitar Hero indicator transforms traditional oscillator signals into a visually engaging, game-like display reminiscent of the popular Guitar Hero video game. Instead of standard line plots, this indicator presents oscillator values as colored segments or blocks, making it easier to quickly identify market conditions at a glance.
Choose from 8 different technical oscillators:
RSI (Relative Strength Index)
Stochastic %K
Stochastic %D
Williams %R
CCI (Commodity Channel Index)
MFI (Money Flow Index)
TSI (True Strength Index)
Ultimate Oscillator
Visual Display Modes
1) Boxes Mode : Creates distinct rectangular boxes for each bar, providing a clean, segmented appearance. (default)
This visual display is limited by the amount of box plots that TradingView allows on each indictor, so it will only plot a limited history. If you want to view a similar visual display that has minor breaks between boxes, then use the fill mode.
2) Fill Mode : Uses filled areas between plot boundaries.
Use this mode when you want to view the plots further back in history without the strict drawing limitations.
Five-Level Color-Coded System
The indicator normalizes all oscillator values to a 0-100 scale and categorizes them into five distinct levels:
Level 1 (Red): Very Oversold (0-19)
Level 2 (Orange): Oversold (20-29)
Level 3 (Yellow): Neutral (30-70)
Level 4 (Aqua): Overbought (71-80)
Level 5 (Lime): Very Overbought (81-100)
Customization Options
Signal Parameters
Signal Length: Primary period for oscillator calculation (default: 14)
Signal Length 2: Secondary period for Stochastic %D and TSI (default: 3)
Signal Length 3: Tertiary period for TSI calculation (default: 25)
Display Controls
Show Horizontal Reference Lines: Toggle grid lines for better level identification
Show Information Table: Display current signal type, value, and normalized value
Table Position: Choose from 9 different screen positions for the info table
Display Mode: Switch between Boxes and Fills visualization
Max Bars to Display: Control how many historical bars to show (50-450 range)
Normalization Process
The indicator automatically normalizes different oscillator ranges to a consistent 0-100 scale:
Williams %R: Converts from -100/0 range to 0-100
CCI: Maps typical -300/+300 range to 0-100
TSI: Transforms -100/+100 range to 0-100
Other oscillators: Already use 0-100 scale (RSI, Stochastic, MFI, Ultimate Oscillator)
This was designed as an educational tool
The gamified approach makes learning about oscillators more engaging for new traders.
FlowScape PredictorFlowScape Predictor is a non-repainting, regime-aware entry qualifier that turns complex market context into two readiness scores (Long & Short, each 0/25/50/75/100) and clean, confirmed-bar signals. It blends three orthogonal pillars so you act only when trend energy, momentum, and location agree:
Regime (energy): ATR-normalized linear-regression slope of a smooth HMA → EMA baseline, gated by ADX to confirm when pressure is meaningful.
Momentum (push): RSI slope alignment so price has directional follow-through, not just drift.
Structure (location): proximity to pivot-confirmed swings, scaled by ATR, so “ready” appears near constructive pullbacks—not mid-trend chases.
A soft ATR cloud wraps the baseline for context. A yellow Predictive Baseline extends beyond the last bar to visualize near-term trajectory. It is visual-only: scores/alerts never use it.
What you see
Baseline line that turns green/red when regime is strong in that direction; gray when weak.
ATR cloud around the baseline (context for stretch and pullbacks).
Scores (Long & Short, 0–100 in steps of 25) and optional “L/S” icons on bar close.
Yellow Predictive Baseline that extends to the right for a few bars (visual trajectory of the smoothed baseline).
The scoring system (simple and transparent)
Each side (Long/Short) sums four binary checks, 25 points each:
Regime aligned: trendStrong is true and LR slope sign favors that side.
Momentum aligned: RSI side (>50 for Long, <50 for Short) and RSI slope confirms direction.
Baseline side: price is above (Long) / below (Short) the baseline.
Location constructive: distance from the last confirmed pivot is healthy (ATR-scaled; not overstretched).
Valid totals are 0, 25, 50, 75, 100.
Best-quality signal: 100/0 (your side/opposite) on bar close.
Good, still valid: 75/0, especially when the missing block is only “location” right as price re-engages the cloud/baseline.
Avoid: 75/25 or any opposition > 0 in a weak (gray) regime.
The Predictive (Kalman) line — what it is and isn’t
The yellow line is a visual forward extension of the smoothed baseline to help you see the current trajectory and time pullback resumptions. It does not predict price and is excluded from scores and alerts.
How it’s built (plain English):
We maintain a one-dimensional Kalman state x as a smoothed estimate of the baseline. Each bar we observe the current baseline z.
The filter adjusts its trust using the Kalman gain K = P / (P + R) and updates:
x := x + K*(z − x), then P := (1 − K)*P + Q.
Q (process noise): Higher Q → expects faster change → tracks turns quicker (less smoothing).
R (measurement noise): Higher R → trusts raw baseline less → smoother, steadier projection.
What you control:
Lead (how many bars forward to draw).
Kalman Q/R (visual smoothness vs. responsiveness).
Toggle the line on/off if you prefer a minimal chart.
Important: The predictive line extends the baseline, not price. It’s a visual timing aid—don’t automate off it.
How to use (step-by-step)
Keep the chart clean and use a standard OHLC/candlestick chart.
Read the regime: Prefer trades with green/red baseline (trendStrong = true).
Check scores on bar close:
Take Long 100 / Short 0 or Long 75 / Short 0 when the chart shows a tidy pullback re-engaging the cloud/baseline.
Mirror the logic for shorts.
Confirm location: If price is > ~1.5 ATR from its reference pivot, let it come back—avoid chasing.
Set alerts: Add an alert on Long Ready or Short Ready; these fire on closed bars only.
Risk management: Use ATR-buffered stops beyond the recent pivot; target fixed-R multiples (e.g., 1.5–3.0R). Manage the trade with the baseline/cloud if you trail.
Best-practice playbook (quick rules)
Green light: 100/0 (best) or 75/0 (good) on bar close in a colored (non-gray) regime.
Location first: Prefer entries near the baseline/cloud right after a pullback, not far above/below it.
Avoid mixed signals: Skip 75/25 and anything with opposition while the baseline is gray.
Use the yellow line with discretion: It helps you see rhythm; it’s not a signal source.
Timeframes & tuning (practical defaults)
Intraday indices/FX (5m–15m): Demand 100/0 in chop; allow 75/0 when ADX is awake and pullback is clean.
Crypto intraday (15m–1h): Prefer 100/0; 75/0 on the first pullback after a regime turn.
Swing (1h–4h/D1): 75/0 is often sufficient; 100/0 is excellent (fewer but cleaner signals).
If choppy: raise ADX threshold, raise the readiness bar (insist on 100/0), or lengthen the RSI slope window.
What makes FlowScape different
Energy-first regime filter: ATR-normalized LR slope + ADX gate yields a consistent read of trend quality across symbols and timeframes.
Location-aware entries: ATR-scaled pivot proximity discourages mid-air chases, encouraging pullback timing.
Separation of concerns: The predictive line is visual-only, while scores/alerts are confirmed on close for non-repainting behavior.
One simple score per side: A single 0–100 readiness figure is easier to tune than juggling multiple indicators.
Transparency & limitations
Scores are coarse by design (25-point blocks). They’re a gatekeeper, not a promise of outcomes.
Pivots confirm after right-side bars, so structure signals appear after swings form (non-repainting by design).
Avoid using non-standard chart types (Heikin Ashi, Renko, Range, etc.) for signals; use a clean, standard chart.
No lookahead, no higher-timeframe requests; alerts fire on closed bars only.
Wavelet-Trend ML Integration [Alpha Extract]Alpha-Extract Volatility Quality Indicator
The Alpha-Extract Volatility Quality (AVQ) Indicator provides traders with deep insights into market volatility by measuring the directional strength of price movements. This sophisticated momentum-based tool helps identify overbought and oversold conditions, offering actionable buy and sell signals based on volatility trends and standard deviation bands.
🔶 CALCULATION
The indicator processes volatility quality data through a series of analytical steps:
Bar Range Calculation: Measures true range (TR) to capture price volatility.
Directional Weighting: Applies directional bias (positive for bullish candles, negative for bearish) to the true range.
VQI Computation: Uses an exponential moving average (EMA) of weighted volatility to derive the Volatility Quality Index (VQI).
Smoothing: Applies an additional EMA to smooth the VQI for clearer signals.
Normalization: Optionally normalizes VQI to a -100/+100 scale based on historical highs and lows.
Standard Deviation Bands: Calculates three upper and lower bands using standard deviation multipliers for volatility thresholds.
Signal Generation: Produces overbought/oversold signals when VQI reaches extreme levels (±200 in normalized mode).
Formula:
Bar Range = True Range (TR)
Weighted Volatility = Bar Range × (Close > Open ? 1 : Close < Open ? -1 : 0)
VQI Raw = EMA(Weighted Volatility, VQI Length)
VQI Smoothed = EMA(VQI Raw, Smoothing Length)
VQI Normalized = ((VQI Smoothed - Lowest VQI) / (Highest VQI - Lowest VQI) - 0.5) × 200
Upper Band N = VQI Smoothed + (StdDev(VQI Smoothed, VQI Length) × Multiplier N)
Lower Band N = VQI Smoothed - (StdDev(VQI Smoothed, VQI Length) × Multiplier N)
🔶 DETAILS
Visual Features:
VQI Plot: Displays VQI as a line or histogram (lime for positive, red for negative).
Standard Deviation Bands: Plots three upper and lower bands (teal for upper, grayscale for lower) to indicate volatility thresholds.
Reference Levels: Horizontal lines at 0 (neutral), +100, and -100 (in normalized mode) for context.
Zone Highlighting: Overbought (⋎ above bars) and oversold (⋏ below bars) signals for extreme VQI levels (±200 in normalized mode).
Candle Coloring: Optional candle overlay colored by VQI direction (lime for positive, red for negative).
Interpretation:
VQI ≥ 200 (Normalized): Overbought condition, strong sell signal.
VQI 100–200: High volatility, potential selling opportunity.
VQI 0–100: Neutral bullish momentum.
VQI 0 to -100: Neutral bearish momentum.
VQI -100 to -200: High volatility, strong bearish momentum.
VQI ≤ -200 (Normalized): Oversold condition, strong buy signal.
🔶 EXAMPLES
Overbought Signal Detection: When VQI exceeds 200 (normalized), the indicator flags potential market tops with a red ⋎ symbol.
Example: During strong uptrends, VQI reaching 200 has historically preceded corrections, allowing traders to secure profits.
Oversold Signal Detection: When VQI falls below -200 (normalized), a lime ⋏ symbol highlights potential buying opportunities.
Example: In bearish markets, VQI dropping below -200 has marked reversal points for profitable long entries.
Volatility Trend Tracking: The VQI plot and bands help traders visualize shifts in market momentum.
Example: A rising VQI crossing above zero with widening bands indicates strengthening bullish momentum, guiding traders to hold or enter long positions.
Dynamic Support/Resistance: Standard deviation bands act as dynamic volatility thresholds during price movements.
Example: Price reversals often occur near the third standard deviation bands, providing reliable entry/exit points during volatile periods.
🔶 SETTINGS
Customization Options:
VQI Length: Adjust the EMA period for VQI calculation (default: 14, range: 1–50).
Smoothing Length: Set the EMA period for smoothing (default: 5, range: 1–50).
Standard Deviation Multipliers: Customize multipliers for bands (defaults: 1.0, 2.0, 3.0).
Normalization: Toggle normalization to -100/+100 scale and adjust lookback period (default: 200, min: 50).
Display Style: Switch between line or histogram plot for VQI.
Candle Overlay: Enable/disable VQI-colored candles (lime for positive, red for negative).
The Alpha-Extract Volatility Quality Indicator empowers traders with a robust tool to navigate market volatility. By combining directional price range analysis with smoothed volatility metrics, it identifies overbought and oversold conditions, offering clear buy and sell signals. The customizable standard deviation bands and optional normalization provide precise context for market conditions, enabling traders to make informed decisions across various market cycles.
Volatility Quality [Alpha Extract]The Alpha-Extract Volatility Quality (AVQ) Indicator provides traders with deep insights into market volatility by measuring the directional strength of price movements. This sophisticated momentum-based tool helps identify overbought and oversold conditions, offering actionable buy and sell signals based on volatility trends and standard deviation bands.
🔶 CALCULATION
The indicator processes volatility quality data through a series of analytical steps:
Bar Range Calculation: Measures true range (TR) to capture price volatility.
Directional Weighting: Applies directional bias (positive for bullish candles, negative for bearish) to the true range.
VQI Computation: Uses an exponential moving average (EMA) of weighted volatility to derive the Volatility Quality Index (VQI).
vqiRaw = ta.ema(weightedVol, vqiLen)
Smoothing: Applies an additional EMA to smooth the VQI for clearer signals.
Normalization: Optionally normalizes VQI to a -100/+100 scale based on historical highs and lows.
Standard Deviation Bands: Calculates three upper and lower bands using standard deviation multipliers for volatility thresholds.
vqiStdev = ta.stdev(vqiSmoothed, vqiLen)
upperBand1 = vqiSmoothed + (vqiStdev * stdevMultiplier1)
upperBand2 = vqiSmoothed + (vqiStdev * stdevMultiplier2)
upperBand3 = vqiSmoothed + (vqiStdev * stdevMultiplier3)
lowerBand1 = vqiSmoothed - (vqiStdev * stdevMultiplier1)
lowerBand2 = vqiSmoothed - (vqiStdev * stdevMultiplier2)
lowerBand3 = vqiSmoothed - (vqiStdev * stdevMultiplier3)
Signal Generation: Produces overbought/oversold signals when VQI reaches extreme levels (±200 in normalized mode).
Formula:
Bar Range = True Range (TR)
Weighted Volatility = Bar Range × (Close > Open ? 1 : Close < Open ? -1 : 0)
VQI Raw = EMA(Weighted Volatility, VQI Length)
VQI Smoothed = EMA(VQI Raw, Smoothing Length)
VQI Normalized = ((VQI Smoothed - Lowest VQI) / (Highest VQI - Lowest VQI) - 0.5) × 200
Upper Band N = VQI Smoothed + (StdDev(VQI Smoothed, VQI Length) × Multiplier N)
Lower Band N = VQI Smoothed - (StdDev(VQI Smoothed, VQI Length) × Multiplier N)
🔶 DETAILS
Visual Features:
VQI Plot: Displays VQI as a line or histogram (lime for positive, red for negative).
Standard Deviation Bands: Plots three upper and lower bands (teal for upper, grayscale for lower) to indicate volatility thresholds.
Reference Levels: Horizontal lines at 0 (neutral), +100, and -100 (in normalized mode) for context.
Zone Highlighting: Overbought (⋎ above bars) and oversold (⋏ below bars) signals for extreme VQI levels (±200 in normalized mode).
Candle Coloring: Optional candle overlay colored by VQI direction (lime for positive, red for negative).
Interpretation:
VQI ≥ 200 (Normalized): Overbought condition, strong sell signal.
VQI 100–200: High volatility, potential selling opportunity.
VQI 0–100: Neutral bullish momentum.
VQI 0 to -100: Neutral bearish momentum.
VQI -100 to -200: High volatility, strong bearish momentum.
VQI ≤ -200 (Normalized): Oversold condition, strong buy signal.
🔶 EXAMPLES
Overbought Signal Detection: When VQI exceeds 200 (normalized), the indicator flags potential market tops with a red ⋎ symbol.
Example: During strong uptrends, VQI reaching 200 has historically preceded corrections, allowing traders to secure profits.
Oversold Signal Detection: When VQI falls below -200 (normalized), a lime ⋏ symbol highlights potential buying opportunities.
Example: In bearish markets, VQI dropping below -200 has marked reversal points for profitable long entries.
Volatility Trend Tracking: The VQI plot and bands help traders visualize shifts in market momentum.
Example: A rising VQI crossing above zero with widening bands indicates strengthening bullish momentum, guiding traders to hold or enter long positions.
Dynamic Support/Resistance: Standard deviation bands act as dynamic volatility thresholds during price movements.
Example: Price reversals often occur near the third standard deviation bands, providing reliable entry/exit points during volatile periods.
🔶 SETTINGS
Customization Options:
VQI Length: Adjust the EMA period for VQI calculation (default: 14, range: 1–50).
Smoothing Length: Set the EMA period for smoothing (default: 5, range: 1–50).
Standard Deviation Multipliers: Customize multipliers for bands (defaults: 1.0, 2.0, 3.0).
Normalization: Toggle normalization to -100/+100 scale and adjust lookback period (default: 200, min: 50).
Display Style: Switch between line or histogram plot for VQI.
Candle Overlay: Enable/disable VQI-colored candles (lime for positive, red for negative).
The Alpha-Extract Volatility Quality Indicator empowers traders with a robust tool to navigate market volatility. By combining directional price range analysis with smoothed volatility metrics, it identifies overbought and oversold conditions, offering clear buy and sell signals. The customizable standard deviation bands and optional normalization provide precise context for market conditions, enabling traders to make informed decisions across various market cycles.
RSI Weighted Trend System I [InvestorUnknown]The RSI Weighted Trend System I is an experimental indicator designed to combine both slow-moving trend indicators for stable trend identification and fast-moving indicators to capture potential major turning points in the market. The novelty of this system lies in the dynamic weighting mechanism, where fast indicators receive weight based on the current Relative Strength Index (RSI) value, thus providing a flexible tool for traders seeking to adapt their strategies to varying market conditions.
Dynamic RSI-Based Weighting System
The core of the indicator is the dynamic weighting of fast indicators based on the value of the RSI. In essence, the higher the absolute value of the RSI (whether positive or negative), the higher the weight assigned to the fast indicators. This enables the system to capture rapid price movements around potential turning points.
Users can choose between a threshold-based or continuous weight system:
Threshold-Based Weighting: Fast indicators are activated only when the absolute RSI value exceeds a user-defined threshold. Below this threshold, fast indicators receive no weight.
Continuous Weighting: By setting the weight threshold to zero, the fast indicators always receive some weight, although this can result in more false signals in ranging markets.
// Calculate weight for Fast Indicators based on RSI (Slow Indicator weight is kept to 1 for simplicity)
f_RSI_Weight_System(series float rsi, simple float weight_thre) =>
float fast_weight = na
float slow_weight = na
if weight_thre > 0
if math.abs(rsi) <= weight_thre
fast_weight := 0
slow_weight := 1
else
fast_weight := 0 + math.sqrt(math.abs(rsi))
slow_weight := 1
else
fast_weight := 0 + math.sqrt(math.abs(rsi))
slow_weight := 1
Slow and Fast Indicators
Slow Indicators are designed to identify stable trends, remaining constant in weight. These include:
DMI (Directional Movement Index) For Loop
CCI (Commodity Channel Index) For Loop
Aroon For Loop
Fast Indicators are more responsive and designed to spot rapid trend shifts:
ZLEMA (Zero-Lag Exponential Moving Average) For Loop
IIRF (Infinite Impulse Response Filter) For Loop
Each of these indicators is calculated using a for-loop method to generate a moving average, which captures the trend of a given length range.
RSI Normalization
To facilitate the weighting system, the RSI is normalized from its usual 0-100 range to a -1 to 1 range. This allows for easy scaling when calculating weights and helps the system adjust to rapidly changing market conditions.
// Normalize RSI (1 to -1)
f_RSI(series float rsi_src, simple int rsi_len, simple string rsi_wb, simple string ma_type, simple int ma_len) =>
output = switch rsi_wb
"RAW RSI" => ta.rsi(rsi_src, rsi_len)
"RSI MA" => ma_type == "EMA" ? (ta.ema(ta.rsi(rsi_src, rsi_len), ma_len)) : (ta.sma(ta.rsi(rsi_src, rsi_len), ma_len))
Signal Calculation
The final trading signal is a weighted average of both the slow and fast indicators, depending on the calculated weights from the RSI. This ensures a balanced approach, where slow indicators maintain overall trend guidance, while fast indicators provide timely entries and exits.
// Calculate Signal (as weighted average)
sig = math.round(((DMI*slow_w) + (CCI*slow_w) + (Aroon*slow_w) + (ZLEMA*fast_w) + (IIRF*fast_w)) / (3*slow_w + 2*fast_w), 2)
Backtest Mode and Performance Metrics
This version of the RSI Weighted Trend System includes a comprehensive backtesting mode, allowing users to evaluate the performance of their selected settings against a Buy & Hold strategy. The backtesting includes:
Equity calculation based on the signals generated by the indicator.
Performance metrics table comparing Buy & Hold strategy metrics with the system’s signals, including: Mean, positive, and negative return percentages, Standard deviations (of all, positive and negative returns), Sharpe Ratio, Sortino Ratio, and Omega Ratio
f_PerformanceMetrics(series float base, int Lookback, simple float startDate, bool Annualize = true) =>
// Initialize variables for positive and negative returns
pos_sum = 0.0
neg_sum = 0.0
pos_count = 0
neg_count = 0
returns_sum = 0.0
returns_squared_sum = 0.0
pos_returns_squared_sum = 0.0
neg_returns_squared_sum = 0.0
// Loop through the past 'Lookback' bars to calculate sums and counts
if (time >= startDate)
for i = 0 to Lookback - 1
r = (base - base ) / base
returns_sum += r
returns_squared_sum += r * r
if r > 0
pos_sum += r
pos_count += 1
pos_returns_squared_sum += r * r
if r < 0
neg_sum += r
neg_count += 1
neg_returns_squared_sum += r * r
float export_array = array.new_float(12)
// Calculate means
mean_all = math.round((returns_sum / Lookback) * 100, 2)
mean_pos = math.round((pos_count != 0 ? pos_sum / pos_count : na) * 100, 2)
mean_neg = math.round((neg_count != 0 ? neg_sum / neg_count : na) * 100, 2)
// Calculate standard deviations
stddev_all = math.round((math.sqrt((returns_squared_sum - (returns_sum * returns_sum) / Lookback) / Lookback)) * 100, 2)
stddev_pos = math.round((pos_count != 0 ? math.sqrt((pos_returns_squared_sum - (pos_sum * pos_sum) / pos_count) / pos_count) : na) * 100, 2)
stddev_neg = math.round((neg_count != 0 ? math.sqrt((neg_returns_squared_sum - (neg_sum * neg_sum) / neg_count) / neg_count) : na) * 100, 2)
// Calculate probabilities
prob_pos = math.round((pos_count / Lookback) * 100, 2)
prob_neg = math.round((neg_count / Lookback) * 100, 2)
prob_neu = math.round(((Lookback - pos_count - neg_count) / Lookback) * 100, 2)
// Calculate ratios
sharpe_ratio = math.round(mean_all / stddev_all * (Annualize ? math.sqrt(Lookback) : 1), 2)
sortino_ratio = math.round(mean_all / stddev_neg * (Annualize ? math.sqrt(Lookback) : 1), 2)
omega_ratio = math.round(pos_sum / math.abs(neg_sum), 2)
// Set values in the array
array.set(export_array, 0, mean_all), array.set(export_array, 1, mean_pos), array.set(export_array, 2, mean_neg),
array.set(export_array, 3, stddev_all), array.set(export_array, 4, stddev_pos), array.set(export_array, 5, stddev_neg),
array.set(export_array, 6, prob_pos), array.set(export_array, 7, prob_neu), array.set(export_array, 8, prob_neg),
array.set(export_array, 9, sharpe_ratio), array.set(export_array, 10, sortino_ratio), array.set(export_array, 11, omega_ratio)
// Export the array
export_array
The metrics help traders assess the effectiveness of their strategy over time and can be used to optimize their settings.
Calibration Mode
A calibration mode is included to assist users in tuning the indicator to their specific needs. In this mode, traders can focus on a specific indicator (e.g., DMI, CCI, Aroon, ZLEMA, IIRF, or RSI) and fine-tune it without interference from other signals.
The calibration plot visualizes the chosen indicator's performance against a zero line, making it easy to see how changes in the indicator’s settings affect its trend detection.
Customization and Default Settings
Important Note: The default settings provided are not optimized for any particular market or asset. They serve as a starting point for experimentation. Traders are encouraged to calibrate the system to suit their own trading strategies and preferences.
The indicator allows deep customization, from selecting which indicators to use, adjusting the lengths of each indicator, smoothing parameters, and the RSI weight system.
Alerts
Traders can set alerts for both long and short signals when the indicator flips, allowing for automated monitoring of potential trading opportunities.
Cantom Chart - CL CTG vs BKDEnglish : This Pine Script indicator, named "Cantom Chart - CL CTG vs BKD," uniquely analyzes the immediate state of oil futures contracts to determine if they are in contango or backwardation. The script uses the price ratio between the nearest (CL1) and the next nearest (CL2) NYMEX crude oil futures contracts. It multiplies this ratio by 100 for clarity and scales fluctuations for enhanced visibility.
Key Features:
Dynamic Ratio Calculation: Computes the ratio (CL1/CL2 * 100) to determine the immediate market state.
Market State Interpretation: A ratio above 100 indicates backwardation, suggesting higher demand than supply, while a ratio below 100 indicates contango, suggesting higher supply than demand.
Volatility Adjustment: Amplifies market state changes by tripling the deviation from the baseline of 100, making it easier to observe subtle shifts.
Anomaly Detection: Caps the adjusted ratio at 125 for highs and 75 for lows, maintaining these limits until the ratio returns to normal levels.
Usage: This indicator is especially useful for traders analyzing supply-demand dynamics and inflationary pressures in the oil market. To apply it, simply add the script to your TradingView chart and adjust the 'Lower Threshold' and 'Upper Threshold' lines as needed based on your trading strategy.
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日本語 : この「Cantom Chart - CL CTG vs BKD」Pine Scriptインジケーターは、直近の原油先物契約がコンタンゴまたはバックワーデーションにあるかを特定するための独自の分析を提供します。最近の（CL1）と次の（CL2）NYMEX原油先物契約間の価格比を使用し、この比率に100を掛けて明確性を高め、変動の視認性を向上させます。
主要機能:
動的比率計算: 市場の即時状態を判断するために比率（CL1/CL2 * 100）を計算します。
市場状態の解釈: 比率が100を超える場合はバックワーデーション（需要が供給を上回る）、100未満の場合はコンタンゴ（供給が需要を上回る）を示します。
変動調整: 基準値100からの偏差を3倍にして、微妙な変化を容易に観察できるようにします。
異常値検出: 調整された比率を高値で125、低値で75に制限し、通常のレベルに戻るまでこれらの限界を維持します。
使用方法: このインジケーターは、原油市場における需給ダイナミクスとインフレ圧力を分析するトレーダーにとって特に有用です。使用するには、このスクリプトをTradingViewチャートに追加し、トレーディング戦略に基づいて「Lower Threshold」と「Upper Threshold」のラインを必要に応じて調整します。
Trend Forecasting - The Quant Science🌏 Trend Forecasting | ENG 🌏
This plug-in acts as a statistical filter, adding new information to your chart that will allow you to quickly verify the direction of a trend and the probability with which the price will be above or below the average in the future, helping you to uncover probable market inefficiencies.
🧠 Model calculation
The model calculates the arithmetic mean in relation to positive and negative events within the available sample for the selected time series. Where a positive event is defined as a closing price greater than the average, and a negative event as a closing price less than the average. Once all events have been calculated, the probabilities are extrapolated by relating each event.
Example
Positive event A: 70
Negative event B: 30
Total events: 100
Probabilities A: (100 / 70) x 100 = 70%
Probabilities B: (100 / 30) x 100 = 30%
Event A has a 70% probability of occurring compared to Event B which has a 30% probability.
🔍 Information Filter
The data on the graph show the future probabilities of prices being above average (default in green) and the probabilities of prices being below average (default in red).
The information that can be quickly retrieved from this indicator is:
1. Trend: Above-average prices together with a constant of data in green greater than 50% + 1 indicate that the observed historical series shows a bullish trend. The probability is correlated proportionally to the value of the data; the higher and increasing the expected value, the greater the observed bullish trend. On the other hand, a below-average price together with a red-coloured data constant show quantitative data regarding the presence of a bearish trend.
2. Future Probability: By analysing the data, it is possible to find the probability with which the price will be above or below the average in the future. In green are classified the probabilities that the price will be higher than the average, in red are classified the probabilities that the price will be lower than the average.
🔫 Operational Filter .
The indicator can be used operationally in the search for investment or trading opportunities given its ability to identify an inefficiency within the observed data sample.
⬆ Bullish forecast
For bullish trades, the inefficiency will appear as a historical series with a bullish trend, with high probability of a bullish trend in the future that is currently below the average.
⬇ Bearish forecast
For short trades, the inefficiency will appear as a historical series with a bearish trend, with a high probability of a bearish trend in the future that is currently above the average.
📚 Settings
Input: via the Input user interface, it is possible to adjust the periods (1 to 500) with which the average is to be calculated. By default the periods are set to 200, which means that the average is calculated by taking the last 200 periods.
Style: via the Style user interface it is possible to adjust the colour and switch a specific output on or off.
🇮🇹Previsione Della Tendenza Futura | ITA 🇮🇹
Questo plug-in funge da filtro statistico, aggiungendo nuove informazioni al tuo grafico che ti permetteranno di verificare rapidamente tendenza di un trend, probabilità con la quale il prezzo si troverà sopra o sotto la media in futuro aiutandoti a scovare probabili inefficienze di mercato.
🧠 Calcolo del modello
Il modello calcola la media aritmetica in relazione con gli eventi positivi e negativi all'intero del campione disponibile per la serie storica selezionata. Dove per evento positivo si intende un prezzo alla chiusura maggiore della media, mentre per evento negativo si intende un prezzo alla chiusura minore della media. Calcolata la totalità degli eventi le probabilità vengono estrapolate rapportando ciascun evento.
Esempio
Evento positivo A: 70
Evento negativo B: 30
Totale eventi : 100
Formula A: (100 / 70) x 100 = 70%
Formula B: (100 / 30) x 100 = 30%
Evento A ha una probabilità del 70% di realizzarsi rispetto all' Evento B che ha una probabilità pari al 30%.
🔍 Filtro informativo
I dati sul grafico mostrano le probabilità future che i prezzi siano sopra la media (di default in verde) e le probabilità che i prezzi siano sotto la media (di default in rosso).
Le informazioni che si possono rapidamente reperire da questo indicatore sono:
1. Trend: I prezzi sopra la media insieme ad una costante di dati in verde maggiori al 50% + 1 indicano che la serie storica osservata presenta un trend rialzista. La probabilità è correlata proporzionalmente al valore del dato; tanto più sarà alto e crescente il valore atteso e maggiore sarà la tendenza rialzista osservata. Viceversa, un prezzo sotto la media insieme ad una costante di dati classificati in colore rosso mostrano dati quantitativi riguardo la presenza di una tendenza ribassista.
2. Probabilità future: analizzando i dati è possibile reperire la probabilità con cui il prezzo si troverà sopra o sotto la media in futuro. In verde vengono classificate le probabilità che il prezzo sarà maggiore alla media, in rosso vengono classificate le probabilità che il prezzo sarà minore della media.
🔫 Filtro operativo
L' indicatore può essere utilizzato a livello operativo nella ricerca di opportunità di investimento o di trading vista la capacità di identificare un inefficienza all'interno del campione di dati osservato.
⬆ Previsione rialzista
Per operatività di tipo rialzista l'inefficienza apparirà come una serie storica a tendenza rialzista, con alte probabilità di tendenza rialzista in futuro che attualmente si trova al di sotto della media.
⬇ Previsione ribassista
Per operatività di tipo short l'inefficienza apparirà come una serie storica a tendenza ribassista, con alte probabilità di tendenza ribassista in futuro che si trova attualmente sopra la media.
📚 Impostazioni
Input: tramite l'interfaccia utente Input è possibile regolare i periodi (da 1 a 500) con cui calcolare la media. Di default i periodi sono impostati sul valore di 200, questo significa che la media viene calcolata prendendo gli ultimi 200 periodi.
Style: tramite l'interfaccia utente Style è possibile regolare il colore e attivare o disattivare un specifico output.
Stochastic RSI of Smoothed Price [Loxx]What is Stochastic RSI of Smoothed Price?
This indicator is just as it's title suggests. There are six different signal types, various price smoothing types, and seven types of RSI.
This indicator contains 7 different types of RSI:
RSX
Regular
Slow
Rapid
Harris
Cuttler
Ehlers Smoothed
What is RSI?
RSI stands for Relative Strength Index . It is a technical indicator used to measure the strength or weakness of a financial instrument's price action.
The RSI is calculated based on the price movement of an asset over a specified period of time, typically 14 days, and is expressed on a scale of 0 to 100. The RSI is considered overbought when it is above 70 and oversold when it is below 30.
Traders and investors use the RSI to identify potential buy and sell signals. When the RSI indicates that an asset is oversold, it may be considered a buying opportunity, while an overbought RSI may signal that it is time to sell or take profits.
It's important to note that the RSI should not be used in isolation and should be used in conjunction with other technical and fundamental analysis tools to make informed trading decisions.
What is RSX?
Jurik RSX is a technical analysis indicator that is a variation of the Relative Strength Index Smoothed ( RSX ) indicator. It was developed by Mark Jurik and is designed to help traders identify trends and momentum in the market.
The Jurik RSX uses a combination of the RSX indicator and an adaptive moving average (AMA) to smooth out the price data and reduce the number of false signals. The adaptive moving average is designed to adjust the smoothing period based on the current market conditions, which makes the indicator more responsive to changes in price.
The Jurik RSX can be used to identify potential trend reversals and momentum shifts in the market. It oscillates between 0 and 100, with values above 50 indicating a bullish trend and values below 50 indicating a bearish trend . Traders can use these levels to make trading decisions, such as buying when the indicator crosses above 50 and selling when it crosses below 50.
The Jurik RSX is a more advanced version of the RSX indicator, and while it can be useful in identifying potential trade opportunities, it should not be used in isolation. It is best used in conjunction with other technical and fundamental analysis tools to make informed trading decisions.
What is Slow RSI?
Slow RSI is a variation of the traditional Relative Strength Index ( RSI ) indicator. It is a more smoothed version of the RSI and is designed to filter out some of the noise and short-term price fluctuations that can occur with the standard RSI .
The Slow RSI uses a longer period of time than the traditional RSI , typically 21 periods instead of 14. This longer period helps to smooth out the price data and makes the indicator less reactive to short-term price fluctuations.
Like the traditional RSI , the Slow RSI is used to identify potential overbought and oversold conditions in the market. It oscillates between 0 and 100, with values above 70 indicating overbought conditions and values below 30 indicating oversold conditions. Traders often use these levels as potential buy and sell signals.
The Slow RSI is a more conservative version of the RSI and can be useful in identifying longer-term trends in the market. However, it can also be slower to respond to changes in price, which may result in missed trading opportunities. Traders may choose to use a combination of both the Slow RSI and the traditional RSI to make informed trading decisions.
What is Rapid RSI?
Same as regular RSI but with a faster calculation method
What is Harris RSI?
Harris RSI is a technical analysis indicator that is a variation of the Relative Strength Index ( RSI ). It was developed by Larry Harris and is designed to help traders identify potential trend changes and momentum shifts in the market.
The Harris RSI uses a different calculation formula compared to the traditional RSI . It takes into account both the opening and closing prices of a financial instrument, as well as the high and low prices. The Harris RSI is also normalized to a range of 0 to 100, with values above 50 indicating a bullish trend and values below 50 indicating a bearish trend .
Like the traditional RSI , the Harris RSI is used to identify potential overbought and oversold conditions in the market. It oscillates between 0 and 100, with values above 70 indicating overbought conditions and values below 30 indicating oversold conditions. Traders often use these levels as potential buy and sell signals.
The Harris RSI is a more advanced version of the RSI and can be useful in identifying longer-term trends in the market. However, it can also generate more false signals than the standard RSI . Traders may choose to use a combination of both the Harris RSI and the traditional RSI to make informed trading decisions.
What is Cuttler RSI?
Cuttler RSI is a technical analysis indicator that is a variation of the Relative Strength Index ( RSI ). It was developed by Curt Cuttler and is designed to help traders identify potential trend changes and momentum shifts in the market.
The Cuttler RSI uses a different calculation formula compared to the traditional RSI . It takes into account the difference between the closing price of a financial instrument and the average of the high and low prices over a specified period of time. This difference is then normalized to a range of 0 to 100, with values above 50 indicating a bullish trend and values below 50 indicating a bearish trend .
Like the traditional RSI , the Cuttler RSI is used to identify potential overbought and oversold conditions in the market. It oscillates between 0 and 100, with values above 70 indicating overbought conditions and values below 30 indicating oversold conditions. Traders often use these levels as potential buy and sell signals.
The Cuttler RSI is a more advanced version of the RSI and can be useful in identifying longer-term trends in the market. However, it can also generate more false signals than the standard RSI . Traders may choose to use a combination of both the Cuttler RSI and the traditional RSI to make informed trading decisions.
What is Ehlers Smoothed RSI?
Ehlers smoothed RSI is a technical analysis indicator that is a variation of the Relative Strength Index ( RSI ). It was developed by John Ehlers and is designed to help traders identify potential trend changes and momentum shifts in the market.
The Ehlers smoothed RSI uses a different calculation formula compared to the traditional RSI . It uses a smoothing algorithm that is designed to reduce the noise and random fluctuations that can occur with the standard RSI . The smoothing algorithm is based on a concept called "digital signal processing" and is intended to improve the accuracy of the indicator.
Like the traditional RSI , the Ehlers smoothed RSI is used to identify potential overbought and oversold conditions in the market. It oscillates between 0 and 100, with values above 70 indicating overbought conditions and values below 30 indicating oversold conditions. Traders often use these levels as potential buy and sell signals.
The Ehlers smoothed RSI can be useful in identifying longer-term trends and momentum shifts in the market. However, it can also generate more false signals than the standard RSI . Traders may choose to use a combination of both the Ehlers smoothed RSI and the traditional RSI to make informed trading decisions.
What is Stochastic RSI?
Stochastic RSI (StochRSI) is a technical analysis indicator that combines the concepts of the Stochastic Oscillator and the Relative Strength Index (RSI). It is used to identify potential overbought and oversold conditions in financial markets, as well as to generate buy and sell signals based on the momentum of price movements.
To understand Stochastic RSI, let's first define the two individual indicators it is based on:
Stochastic Oscillator: A momentum indicator that compares a particular closing price of a security to a range of its prices over a certain period. It is used to identify potential trend reversals and generate buy and sell signals.
Relative Strength Index (RSI): A momentum oscillator that measures the speed and change of price movements. It ranges between 0 and 100 and is used to identify overbought or oversold conditions in the market.
Now, let's dive into the Stochastic RSI:
The Stochastic RSI applies the Stochastic Oscillator formula to the RSI values, essentially creating an indicator of an indicator. It helps to identify when the RSI is in overbought or oversold territory with more sensitivity, providing more frequent signals than the standalone RSI.
The formula for StochRSI is as follows:
StochRSI = (RSI - Lowest Low RSI) / (Highest High RSI - Lowest Low RSI)
Where:
RSI is the current RSI value.
Lowest Low RSI is the lowest RSI value over a specified period (e.g., 14 days).
Highest High RSI is the highest RSI value over the same specified period.
StochRSI ranges from 0 to 1, but it is usually multiplied by 100 for easier interpretation, making the range 0 to 100. Like the RSI, values close to 0 indicate oversold conditions, while values close to 100 indicate overbought conditions. However, since the StochRSI is more sensitive, traders typically use 20 as the oversold threshold and 80 as the overbought threshold.
Traders use the StochRSI to generate buy and sell signals by looking for crossovers with a signal line (a moving average of the StochRSI), similar to the way the Stochastic Oscillator is used. When the StochRSI crosses above the signal line, it is considered a bullish signal, and when it crosses below the signal line, it is considered a bearish signal.
It is essential to use the Stochastic RSI in conjunction with other technical analysis tools and indicators, as well as to consider the overall market context, to improve the accuracy and reliability of trading signals.
Signal types included are the following;
Fixed Levels
Floating Levels
Quantile Levels
Fixed Middle
Floating Middle
Quantile Middle
Extras
Alerts
Bar coloring
Loxx's Expanded Source Types
Copy/Paste LevelsCopy/Paste Levels allows levels to be pasted onto your chart from a properly formatted source.
This tool streamlines the process of adding lines to your chart, and sharing lines from your chart.
More than one ticker at a time!
This indicator will only draw lines on charts it has values for!
This means you can input levels for every ticker you need all at once, one time, and only be displayed the levels for the current chart you are looking at. When you switch tickers, the levels for that ticker will display. (Assuming you have levels entered for that ticker)
The formatting is as follows:
Ticker,Color,Style,Width,Lvl1,Lvl2,Lvl3;
Ticker - Any ticker on Tradingview can be used in the field
Color - Available colors are: Red,Orange,Yellow,Green,Blue,Purple,White,Black,Gray
Style - Available styles are: Solid,Dashed,Dotted
Width - This can be any negative integer, ex.(-1,-2,-3,-4,-5)
Lvls - These can be any positive number (decimals allowed)
Semi-Colons separate sections, each section contains enough information to create at least 1 line.
Each additional level added within the same section will have the same styling parameters as the other levels in the section.
Example:
2 solid lines colored red with a thickness of 2 on QQQ, 1 at $300 and 1 at $400.
QQQ,RED,SOLID,-2,300,400;
IMPORTANT MUST READ!!!
Remember to not include any spaces between commas and the entries in each field!
ex. ; QQQ, red, dotted, -1, 325; <- Wrong
ex. ;QQQ,red,dotted,-1,325;)<- Right
However,
All fields must be filled out, to use default values in the fields, insert a space between the commas.
ex. ;QQQ,red,dotted,,325; <- Wrong
ex. ;QQQ,red,dotted, ,325; <- Right
While spaces can not be included line breaks can!
I recommend for easier typing and viewing to include a line break for each new line (if changing styling or ticker)
Example:
2 solid lines, one red at $300, one green at $400, both default width. Written in a single line AND using multiple lines, both give the same output.
QQQ,red,solid, ,300;QQQ,green,solid, ,400;
or
QQQ,red,solid, ,300;
QQQ,green,solid, ,400;
In this following screenshot you can see more examples of different formatting variations.
The textbox contains exactly what is pasted into the settings input box.
As you can see, capitalization does not matter.
Default Values:
Color = optimal contrast color, If this field is filled in with a space it will display the optimal contrast color of the users background.
Style = solid
Width = -1
More Examples:
Multi-Ticker: drawing 3 lines at $300, all default values, on 3 different tickers
SPY, , , ,300;QQQ, , , ,300;AAPL, , , ,300
or
SPY, , , ,300;
QQQ, , , ,300;
AAPL, , , ,300
Multiple levels: There is no limit* to the number of levels that can be included within 1 section.
* only TV default line limit per indicator (500)
This will be 4 lines all with the same styling at different values on 2 separate tickers.
SPY,BLUE,SOLID,-2,100,200,300,400;QQQ,BLUE,SOLID,-2,100,200,300,400
or
SPY,BLUE,SOLID,-2,100,200,300,400;
QQQ,BLUE,SOLID,-2,100,200,300,400
Semi-colons must separate sections, but are not required at the beginning or end, it makes no difference if they are or are not added.
SPY,BLUE,SOLID,-2,100,200,300,400;
QQQ,BLUE,SOLID,-2,100,200,300,400
==
SPY,BLUE,SOLID,-2,100,200,300,400;
QQQ,BLUE,SOLID,-2,100,200,300,400;
==
;SPY,BLUE,SOLID,-2,100,200,300,400;
QQQ,BLUE,SOLID,-2,100,200,300,400;
All the above output the same results.
Hope this is helpful for people,
Enjoy!
HSM TOOLS//@version=5
indicator("HSM TOOLS", overlay=true, max_lines_count=500, max_labels_count=5, max_boxes_count=500)
// General Settings Inputs
TZI = input.string (defval="UTC -4", title="Timezone Selection", options= , tooltip="Select the Timezone. ( Shifts Chart Elements )", group="Global Settings")
Timezone = TZI == "UTC -10" ? "GMT-10:00" : TZI == "UTC -7" ? "GMT-07:00" : TZI == "UTC -6" ? "GMT-06:00" : TZI == "UTC -5" ? "GMT-05:00" : TZI == "UTC -4" ? "GMT-04:00" : TZI == "UTC -3" ? "GMT-03:00" : TZI == "UTC +0" ? "GMT+00:00" : TZI == "UTC +1" ? "GMT+01:00" : TZI == "UTC +2" ? "GMT+02:00" : TZI == "UTC +3" ? "GMT+03:00" : TZI == "UTC +3:30" ? "GMT+03:30" : TZI == "UTC +4" ? "GMT+04:00" : TZI == "UTC +5" ? "GMT+05:00" : TZI == "UTC +5:30" ? "GMT+05:30" : TZI == "UTC +6" ? "GMT+06:00" : TZI == "UTC +7" ? "GMT+07:00" : TZI == "UTC +8" ? "GMT+08:00" : TZI == "UTC +9" ? "GMT+09:00" : TZI == "UTC +9:30" ? "GMT+09:30" : TZI == "UTC +10" ? "GMT+10:00" : TZI == "UTC +10:30" ? "GMT+10:30" : TZI == "UTC +11" ? "GMT+11:00" : TZI == "UTC +13" ? "GMT+13:00" : "GMT+13:45"
inputMaxInterval = input.int (31, title="Hide Indicator Above Specified Minutes", tooltip="Above 30Min, Chart Will Become Messy & Unreadable", group="Global Settings")
// Session options
ShowTSO = input.bool (true, title="Show Today's Session Only", group="Session Options", tooltip="Hide Historical Sessions")
ShowTWO = input.bool (true, title="Show Current Week's Sessions Only", group="Session Options", tooltip="Show All Sessions from the current week")
SL4W = input.bool (true, title="Show Last 4 Week Sessions", group="Session Options", tooltip="Show All Sessions from Last Four Weeks \nShould Disable Current Week Session to Work")
ShowSFill = input.bool (false, title="Show Session Highlighting", group="Session Options", tooltip="Highlights Session from Top of the Chart to Bottom")
//----------------------------------------------
// Historical Lines
ShowMOPL = input.bool (title="Midnight Historical Price Lines", defval=false, group="Historical Lines", tooltip="Shows Historical Midnight Price Lines")
MOLHist = input.bool (title="Midnight Historical Vertical Lines", defval=true, group="Historical Lines", tooltip="Shows Historical Midnight Vertical Lines")
ShowPrev = input.bool (false, title="Misc. Historical Price Lines", group="Historical Lines", tooltip="Makes Chart Cluttered, Use For Backtesting Only")
//----------------------------------------------
// Session Bool
ShowLondon = input.bool (false, "", inline="LONDON", group="Sessions", tooltip="01:00 to 05:00")
ShowNY = input.bool (false, "", inline="NY", group="Sessions", tooltip="07:00 to 10:00")
ShowLC = input.bool (false, "", inline="LC", group="Sessions", tooltip="10:00 to 12:00")
ShowPM = input.bool (false, "",inline="PM", group="Sessions", tooltip="13:00 to 16:00")
ShowAsian = input.bool (false, "",inline="ASIA2", group="Sessions", tooltip="20:00 to 00:00")
ShowFreeSesh = input.bool (false, "",inline="FREE", group="Sessions", tooltip="Custom Session")
// Session Strings
txt2 = input.string ("LONDON", title="", inline="LONDON", group="Sessions")
txt3 = input.string ("NEW YORK", title="", inline="NY", group="Sessions")
txt4 = input.string ("LDN CLOSE", title="", inline="LC", group="Sessions")
txt5 = input.string ("AFTERNOON", title="", inline="PM", group="Sessions")
txt6 = input.string ("ASIA", title="", inline="ASIA2", group="Sessions")
txt9 = input.string ("FREE SESH", title="", inline="FREE", group="Sessions")
// CBDR = input.session ('1400-2000:1234567', "", inline="CBDR", group="Sessions")
// ASIA = input.session ('2000-0000:1234567', "", inline="ASIA", group="Sessions")
// Session Times
LDNsesh = input.session ('0200-0500:1234567', "", inline="LONDON", group="Sessions")
NYsesh = input.session ('0700-1000:1234567', "", inline="NY", group="Sessions")
LCsesh = input.session ('1000-1200:1234567', "", inline="LC", group="Sessions")
PMsesh = input.session ('1300-1600:1234567', "", inline="PM", group="Sessions")
ASIA2sesh = input.session ('2000-2359:1234567', "", inline="ASIA2", group="Sessions")
FreeSesh = input.session ('0000-0000:1234567', "", inline="FREE", group="Sessions")
// Session Color
LSFC = input.color (color.new(#787b86, 90), "", inline="LONDON", group="Sessions")
NYSFC = input.color (color.new(#787b86, 90), "",inline="NY", group="Sessions")
LCSFC = input.color (color.new(#787b86, 90), "",inline="LC", group="Sessions")
PMSFC = input.color (color.new(#787b86, 90), "",inline="PM", group="Sessions")
ASFC = input.color (color.new(#787b86, 90), "",inline="ASIA2", group="Sessions")
FSFC = input.color (color.new(#787b86, 90), "",inline="FREE", group="Sessions")
//----------------------------------------------
// Vertical Line Bool
ShowMOP = input.bool (title="", defval=true, inline="MOP", group="Vertical Lines", tooltip="00:00 AM")
txt12 = input.string ("MIDNIGHT", title="", inline="MOP", group="Vertical Lines")
ShowLOP = input.bool (title="", defval=false, inline="LOP", group="Vertical Lines", tooltip="03:00 AM")
txt14 = input.string ("LONDON", title="", inline="LOP", group="Vertical Lines")
ShowNYOP = input.bool (title="", defval=true, inline="NYOP", group="Vertical Lines", tooltip="08:30 AM")
txt15 = input.string ("NEW YORK", title="", inline="NYOP", group="Vertical Lines")
ShowEOP = input.bool (title="", defval=false, inline="EOP", group="Vertical Lines", tooltip="09:30 AM")
txt16 = input.string ("EQUITIES", title="", inline="EOP", group="Vertical Lines")
// Vertical Line Color
MOPColor = input.color (color.new(#787b86, 0), "", inline="MOP", group="Vertical Lines")
LOPColor = input.color (color.rgb(0,128,128,60), "", inline="LOP", group="Vertical Lines")
NYOPColor = input.color (color.rgb(0,128,128,60), "", inline="NYOP", group="Vertical Lines")
EOPColor = input.color (color.rgb(0,128,128,60), "", inline="EOP", group="Vertical Lines")
// Vertical LineStyle
Midnight_Open_LS = input.string ("Dotted", "", options= , inline="MOP", group="Vertical Lines")
london_Open_LS = input.string ("Solid", "", options= , inline="LOP", group="Vertical Lines")
NY_Open_LS = input.string ("Solid", "", options= , inline="NYOP", group="Vertical Lines")
Equities_Open_LS = input.string ("Solid", "", options= , inline="EOP", group="Vertical Lines")
// Vertical LineWidth
Midnight_Open_LW = input.string ("1px", "", options= , inline="MOP", group="Vertical Lines")
London_Open_LW = input.string ("1px", "", options= , inline="LOP", group="Vertical Lines")
NY_Open_LW = input.string ("1px", "", options= , inline="NYOP", group="Vertical Lines")
Equities_Open_LW = input.string ("1px", "", options= , inline="EOP", group="Vertical Lines")
//----------------------------------------------
// Opening Price Bool
ShowMOPP = input.bool (title="", defval=true, inline="MOPP", group="Opening Price Lines", tooltip="00:00 AM")
txt13 = input.string ("MIDNIGHT", title="", inline="MOPP", group="Opening Price Lines")
ShowNYOPP = input.bool (title="", defval=false, inline="NYOPP", group="Opening Price Lines", tooltip="08:30 AM")
txt17 = input.string ("NEW YORK", title="", inline="NYOPP", group="Opening Price Lines")
ShowEOPP = input.bool (title="", defval=false, inline="EOPP", group="Opening Price Lines", tooltip="09:30 AM")
txt18 = input.string ("EQUITIES", title="", inline="EOPP", group="Opening Price Lines")
ShowAFTPP = input.bool (title="", defval=false, inline="AFTOPP", group="Opening Price Lines", tooltip="01:30 PM")
txt1330 = input.string ("AFTERNOON", title="", inline="AFTOPP", group="Opening Price Lines")
// Opening Price Color
MOPColP = input.color (color.new(#787b86, 0), "", inline="MOPP", group="Opening Price Lines")
NYOPColP = input.color (color.new(#787b86, 0), "", inline="NYOPP", group="Opening Price Lines")
EOPColP = input.color (color.new(#787b86, 0), "", inline="EOPP", group="Opening Price Lines")
AFTOPColP = input.color (color.new(#787b86, 0), "", inline="AFTOPP", group="Opening Price Lines")
// Opening Price LineStyle
MOPLS = input.string ("Dotted", "", options= , inline="MOPP", group="Opening Price Lines")
NYOPLS = input.string ("Dotted", "", options= , inline="NYOPP", group="Opening Price Lines")
EOPLS = input.string ("Dotted", "", options= , inline="EOPP", group="Opening Price Lines")
AFTOPLS = input.string ("Dotted", "", options= , inline="AFTOPP", group="Opening Price Lines")
// Opening Price LineWidth
i_MOPLW = input.string ("1px", "", options= , inline="MOPP", group="Opening Price Lines")
i_NYOPLW = input.string ("1px", "", options= , inline="NYOPP", group="Opening Price Lines")
i_EOPLW = input.string ("1px", "", options= , inline="EOPP", group="Opening Price Lines")
i_AFTOPLW = input.string ("1px", "", options= , inline="AFTOPP", group="Opening Price Lines")
//----------------------------------------------
// W&M Bool
ShowWeekOpen = input.bool (defval=false, title="", tooltip="Draw Weekly Open Price Line", group="HTF Opening Price Lines", inline="WO")
showMonthOpen = input.bool (defval=false, title="", tooltip="Draw Monthly Open Price Line", group="HTF Opening Price Lines", inline="MO")
// W&M String
txt19 = input.string ("WEEKLY", title="", inline="WO", group="HTF Opening Price Lines")
txt20 = input.string ("MONTHLY", title="", inline="MO", group="HTF Opening Price Lines")
// W&M Color
i_WeekOpenCol = input.color (title="", defval=color.new(#787b86, 0), group="HTF Opening Price Lines", inline="WO")
i_MonthOpenCol = input.color (title="", tooltip="", defval=color.new(#787b86, 0), group="HTF Opening Price Lines", inline="MO")
// W&M LineStyle
WOLS = input.string ("Dotted", "", options= , inline="WO", group="HTF Opening Price Lines")
MOLS = input.string ("Dotted", "", options= , inline="MO", group="HTF Opening Price Lines")
// W&M LineWidth
i_WOPLW = input.string ("1px", "", options= , inline="WO", group="HTF Opening Price Lines")
i_MONPLW = input.string ("1px", "", options= , inline="MO", group="HTF Opening Price Lines")
//----------------------------------------------
// CBDR, ASIA & FLOUT
ShowCBDR = input.bool (true, "", inline='CBDR', group="CBDR, ASIA & FLOUT")
ShowASIA = input.bool (true, "", inline='ASIA', group="CBDR, ASIA & FLOUT")
ShowFLOUT = input.bool (false, "", inline='FLOUT', group="CBDR, ASIA & FLOUT")
// Strings
txt0 = input.string ("CBDR", title="", inline="CBDR", group="CBDR, ASIA & FLOUT", tooltip="16:00 to 20:00 \nSD Increments of 1")
txt1 = input.string ("ASIA", title="", inline="ASIA", group="CBDR, ASIA & FLOUT", tooltip="20:00 to 00:00 \nSD Increments of 1")
txt7 = input.string ("FLOUT", title="", inline="FLOUT", group="CBDR, ASIA & FLOUT", tooltip="16:00 to 00:00 \nSD Increments of 0.5")
// Color
CBDRBoxCol = input.color (color.new(#787b86, 0),"", inline='CBDR', group="CBDR, ASIA & FLOUT")
ASIABoxCol = input.color (color.new(#787b86, 0), "", inline='ASIA', group="CBDR, ASIA & FLOUT")
FLOUTBoxCol = input.color (color.new(#787b86, 0),"", inline='FLOUT', group="CBDR, ASIA & FLOUT")
// Extras
box_text_cbdr = input.bool (true, "Show Text", inline="CBDR", group="CBDR, ASIA & FLOUT")
box_text_cbdr_col = input.color (color.new(color.gray, 80), "", inline="CBDR", group="CBDR, ASIA & FLOUT")
bool_cbdr_dev = input.bool (true, "SD", inline="CBDR", group="CBDR, ASIA & FLOUT")
box_text_asia = input.bool (true, "Show Text", inline="ASIA", group="CBDR, ASIA & FLOUT")
box_text_asia_col = input.color (color.new(color.gray, 80), "", inline="ASIA", group="CBDR, ASIA & FLOUT")
bool_asia_dev = input.bool (true, "SD", inline="ASIA", group="CBDR, ASIA & FLOUT")
box_text_flout = input.bool (true, "Show Text", inline="FLOUT", group="CBDR, ASIA & FLOUT")
box_text_flout_col = input.color (color.new(color.gray, 80), "", inline="FLOUT", group="CBDR, ASIA & FLOUT")
bool_flout_dev = input.bool (true, "SD", inline="FLOUT", group="CBDR, ASIA & FLOUT")
// Table
// SD Lines
ShowDevLN = input.bool (title="", defval=true, inline="DEVLN", group="Standard Deviation", tooltip="Deviation Lines")
DEVLNTXT = input.string ("SD LINES", title="", inline="DEVLN", group="Standard Deviation")
DevLNCol = input.color (color.new(#787b86, 0), "", inline="DEVLN", group="Standard Deviation")
DEVLS = input.string ("Solid", "", options= , inline="DEVLN", group="Standard Deviation")
i_DEVLW = input.string ("1px", "", options= , inline="DEVLN", group="Standard Deviation")
DEVLSS = DEVLS=="Solid" ? line.style_solid : DEVLS == "Dotted" ? line.style_dotted : line.style_dashed
DEVLW = i_DEVLW=="1px" ? 1 : i_DEVLW == "2px" ? 2 : i_DEVLW == "3px" ? 3 : i_DEVLW == "4px" ? 4 : 5
ShowDev = input.bool (false, '', inline="DEV", group="Standard Deviation")
txt8 = input.string ("SD COUNT", title="", inline="DEV", group="Standard Deviation")
SDCountCol = input.color (color.new(#787b86, 0), "", inline="DEV", group="Standard Deviation")
DevInput = input.string ("2 SD", "", options= , inline="DEV", group="Standard Deviation")
DevDirection = input.string ("Both", "", options= , inline="DEV", group="Standard Deviation", tooltip="SD Count, NULL, SD Count, SD Direction")
DevCount = DevInput == "1 SD" ? 1 : DevInput == "2 SD" ? 2 : DevInput == "3 SD" ? 3 : 4
Auto_Select = input.bool (false, "", group="Standard Deviation", inline="AUTOSD", tooltip="Auto SD Selection | Charter Content, Range Table \nMight Bug Out On Mondays" )
txtSD = input.string ("AUTO SD", "", group="Standard Deviation", inline="AUTOSD")
Tab1txtCol = input.color (color.new(#808080, 0), "", inline='AUTOSD', group="Standard Deviation")
TabOptionShow = input.string ("Show Table", "", options= , inline="AUTOSD", group="Standard Deviation")
Stats = TabOptionShow == "Show Table" ? true : false
TabOption1 = input.string ("Top Right", "", options= , inline="AUTOSD", group="Standard Deviation")
tabinp1 = TabOption1 == "Top Left" ? position.top_left : TabOption1 == "Top Center" ? position.top_center : TabOption1 == "Top Right" ? position.top_right : TabOption1 == "Middle Left" ? position.middle_left : TabOption1 == "Middle Right" ? position.middle_right : TabOption1 == "Bottom Left" ? position.bottom_left : TabOption1 == "Bottom Center" ? position.bottom_center : position.bottom_right
L_Prof = true
CellBG = color.new(#131722, 100)
//----------------------------------------------
// Day Of Week & Labels
// Label Settings Inputs
ShowLabel = input.bool (true, title="", inline="Glabel", group="Day Of Week & Labels")
txt21 = input.string ("LABEL", title="", inline="Glabel", group="Day Of Week & Labels")
LabelColor = input.color (color.rgb(0,0,0,100), "", inline="Glabel", group="Day Of Week & Labels")
LabelSizeInput = input.string ("Normal", "", options= , inline="Glabel", group="Day Of Week & Labels")
Terminusinp = input.string ("Terminus @ Current Time +1hr", "", options = , inline="Glabel", group="Day Of Week & Labels", tooltip="Select Label Size & Color & Terminus \nHistorical Price Lines needs to be toggled off for using Terminus")
ShowLabelText = input.bool (true, title="", inline="label", group="Day Of Week & Labels")
txt22 = input.string ("LABEL TEXT", title="", inline="label", group="Day Of Week & Labels")
LabelTextColor = input.color (color.new(#787b86, 0), title="", inline="label", group="Day Of Week & Labels")
LabelTextOptioninput = input.string ("Time", "", options= , inline="label", group="Day Of Week & Labels", tooltip="Choose Between Descriptive Text as Label or Time \nShow/Hide Prices on Labels")
ShowPricesBool = input.string ("Hide Prices", title="", options= , group="Day Of Week & Labels", inline="label")
ShowPrices = ShowPricesBool == "Show Prices" ? true : false
showDOW = input.bool (true, title="", inline="DOW", group="Day Of Week & Labels")
txt24 = input.string ("DAY OF WEEK", title="", inline="DOW", group="Day Of Week & Labels")
i_DOWCol = input.color (color.new(#787b86, 0), title="", inline="DOW", group="Day Of Week & Labels")
DOWTime = input.int (defval = 12, title="", inline="DOW", group="Day Of Week & Labels")
DOWLoc_inpt = input.string ("Bottom", "", options = , inline="DOW", group="Day Of Week & Labels", tooltip="DOW Color, Time Alignment, Vertical Location")
DOWLoc = DOWLoc_inpt == "Bottom" ? location.bottom : location.top
//----------------------------------------------
BIAS_M_Bool = input.bool (false, "", group="BIAS & NOTES PRECONFIG", inline="stats")
txt100 = input.string ("BIAS", title="", inline="stats", group="BIAS & NOTES PRECONFIG")
TableBG2 = color.new(#131722, 100)
Tab2txtCol = input.color (color.new(#787b86, 0), "", inline='stats', group="BIAS & NOTES PRECONFIG")
TabOption2 = input.string ("Bottom Right", "", options= , inline="stats", group="BIAS & NOTES PRECONFIG")
tabinp2 = TabOption2 == "Top Left" ? position.top_left : TabOption2 == "Top Center" ? position.top_center : TabOption2 == "Top Right" ? position.top_right : TabOption2 == "Middle Left" ? position.middle_left : TabOption2 == "Middle Right" ? position.middle_right : TabOption2 == "Bottom Left" ? position.bottom_left : TabOption2 == "Bottom Center" ? position.bottom_center : position.bottom_right
notesbool = false
NOTES_M_Bool = input.bool (true, "", group="BIAS & NOTES PRECONFIG", inline="stats2")
txt101 = input.string ("NOTES", title="", inline="stats2", group="BIAS & NOTES PRECONFIG")
Tab3txtCol = input.color (color.new(#787b86, 0), "", inline='stats2', group="BIAS & NOTES PRECONFIG")
TabOption3 = input.string ("Top Center", "", options= , inline="stats2", group="BIAS & NOTES PRECONFIG")
tabinp3 = TabOption3 == "Top Left" ? position.top_left : TabOption3 == "Top Center" ? position.top_center : TabOption3 == "Top Right" ? position.top_right : TabOption3 == "Middle Left" ? position.middle_left : TabOption3 == "Middle Right" ? position.middle_right : TabOption3 == "Bottom Left" ? position.bottom_left : TabOption3 == "Bottom Center" ? position.bottom_center : position.bottom_right
BIASbool1 = input.bool (true, '', inline="BIAS1", group="BIAS & NOTES")
txt52 = input.string ("DXY ", title="", inline="BIAS1", group="BIAS & NOTES")
BIASOption1 = input.string ("Unclear", options= , title="", inline="BIAS1", group="BIAS & NOTES")
BIASbool2 = input.bool (true, '', inline="BIAS2", group="BIAS & NOTES")
txt53 = input.string ("SPX ", title="", inline="BIAS2", group="BIAS & NOTES")
BIASOption2 = input.string ("Unclear", options= , title="", inline="BIAS2", group="BIAS & NOTES")
BIASbool3 = input.bool (true, '', inline="BIAS3", group="BIAS & NOTES")
txt54 = input.string ("DOW ", title="", inline="BIAS3", group="BIAS & NOTES")
BIASOption3 = input.string ("Unclear", options= , title="", inline="BIAS3", group="BIAS & NOTES")
BIASbool4 = input.bool (true, '', inline="BIAS4", group="BIAS & NOTES")
txt55 = input.string ("NAS ", title="", inline="BIAS4", group="BIAS & NOTES")
BIASOption4 = input.string ("Unclear", options= , title="", inline="BIAS4", group="BIAS & NOTES")
notes = input.text_area ("@hiran.invest", "Notes", group = "BIAS & NOTES")
//--------------------END OF INPUTS--------------------//
// Pre-Def
DOM = (timeframe.multiplier <= inputMaxInterval) and (timeframe.isintraday)
newDay = ta.change(dayofweek)
newWeek = ta.change(weekofyear)
newMonth = ta.change(time("M"))
transparentcol = color.rgb(255,255,255,100)
LSVLC = color.rgb(255,255,255,100)
NYSVLC = color.rgb(255,255,255,100)
PMSVLC = color.rgb(255,255,255,100)
ASVLC = color.rgb(255,255,255,100)
LSVLS = "dotted"
NYSVLS = "dotted"
PMSVLS = "dotted"
ASVLS = "dotted"
// Functions
isToday = false
if year(timenow) == year(time) and month(timenow) == month(time) and dayofmonth(timenow) == dayofmonth(time)
isToday := true
// Current Week
thisweek = year(timenow) == year(time) and weekofyear(timenow) == weekofyear(time)
LastOneWeek = year(timenow) == year(time) and weekofyear(timenow-604800000) == weekofyear(time)
LastTwoWeek = year(timenow) == year(time) and weekofyear(timenow-1209600000) == weekofyear(time)
LastThreeWeek = year(timenow) == year(time) and weekofyear(timenow-1814400000) == weekofyear(time)
LastFourWeek = year(timenow) == year(time) and weekofyear(timenow-2419200000) == weekofyear(time)
Last4Weeks = false
if thisweek == true or LastOneWeek == true or LastTwoWeek == true or LastThreeWeek == true or LastFourWeek == true
Last4Weeks := true
// Function to draw Vertical Lines
vline(Start, Color, linestyle, LineWidth) =>
line.new(x1=Start, y1=low - ta.tr, x2=Start, y2=high + ta.tr, xloc=xloc.bar_time, extend=extend.both, color=Color, style=linestyle, width=LineWidth)
// Function to convert forex pips into whole numbers
atr = ta.atr(14)
toWhole(number) =>
if syminfo.type == "forex" // This method only works on forex pairs
_return = atr < 1.0 ? (number / syminfo.mintick) / 10 : number
_return := atr >= 1.0 and atr < 100.0 and syminfo.currency == "JPY" ? _return * 100 : _return
else
number
// Function for determining the Start of a Session (taken from the Pinescript manual: www.tradingview.com )
SessionBegins(sess) =>
t = time("", sess , Timezone)
DOM and (not barstate.isfirst) and na(t ) and not na(t)
// BarIn Session
BarInSession(sess) =>
time(timeframe.period, sess, Timezone) != 0
// Label Type Logic
var SFistrue = true
if LabelTextOptioninput == "Time"
SFistrue := true
else
SFistrue := false
// Session String to int
SeshStartHour(Session) =>
math.round(str.tonumber(str.substring(Session,0,2)))
SeshStartMins(Session) =>
math.round(str.tonumber(str.substring(Session,2,4)))
SeshEndHour(Session) =>
math.round(str.tonumber(str.substring(Session,5,7)))
SeshEndMins(Session) =>
math.round(str.tonumber(str.substring(Session,7,9)))
// Time periods
CBDR = "1600-2000:1234567"
ASIA = "2000-0000:1234567"
FLOUT = "1600-0000:1234567"
midsesh = "0000-1600:1234567"
cbdrOpenTime = timestamp (Timezone, year, month, dayofmonth, SeshStartHour(CBDR), SeshStartMins(CBDR), 00)
cbdrEndTime = timestamp (Timezone, year, month, dayofmonth, SeshEndHour(CBDR), SeshEndMins(CBDR), 00)
asiaOpenTime = timestamp (Timezone, year, month, dayofmonth, SeshStartHour(ASIA), SeshStartMins(ASIA), 00)
asiaEndTime = timestamp (Timezone, year, month, dayofmonth, SeshEndHour(ASIA), SeshEndMins(ASIA), 00)+86400000
floutOpenTime = timestamp (Timezone, year, month, dayofmonth, SeshStartHour(FLOUT), SeshStartMins(FLOUT), 00)
floutEndTime = timestamp (Timezone, year, month, dayofmonth, SeshEndHour(FLOUT), SeshEndMins(FLOUT), 00)+86400000
CBDRTime = time (timeframe.period, CBDR, Timezone)
ASIATime = time (timeframe.period, ASIA, Timezone)
FLOUTTime = time (timeframe.period, FLOUT, Timezone)
LabelOnlyToday = true
// Time Periods
LondonStartTime = timestamp(Timezone, year, month, dayofmonth, SeshStartHour(LDNsesh), SeshStartMins(LDNsesh), 00)
LondonEndTime = timestamp(Timezone, year, month, dayofmonth, SeshEndHour(LDNsesh), SeshEndMins(LDNsesh), 00)
NYStartTime = timestamp(Timezone, year, month, dayofmonth, SeshStartHour(NYsesh), SeshStartMins(NYsesh), 00)
NYEndTime = timestamp(Timezone, year, month, dayofmonth, SeshEndHour(NYsesh), SeshEndMins(NYsesh), 00)
LCStartTime = timestamp(Timezone, year, month, dayofmonth, SeshStartHour(LCsesh), SeshStartMins(LCsesh), 00)
LCEndTime = timestamp(Timezone, year, month, dayofmonth, SeshEndHour(LCsesh), SeshEndMins(LCsesh), 00)
PMStartTime = timestamp(Timezone, year, month, dayofmonth, SeshStartHour(PMsesh), SeshStartMins(PMsesh), 00)
PMEndTime = timestamp(Timezone, year, month, dayofmonth, SeshEndHour(PMsesh), SeshEndMins(PMsesh), 00)
AsianStartTime = timestamp(Timezone, year, month, dayofmonth, SeshStartHour(ASIA2sesh), SeshStartMins(ASIA2sesh), 00)
AsianEndTime = timestamp(Timezone, year, month, dayofmonth, SeshEndHour(ASIA2sesh), SeshEndMins(ASIA2sesh), 00)
FreeStartTime = timestamp(Timezone, year, month, dayofmonth, SeshStartHour(FreeSesh), SeshStartMins(FreeSesh), 00)
FreeEndTime = timestamp(Timezone, year, month, dayofmonth, SeshEndHour(FreeSesh), SeshEndMins(FreeSesh), 00)
MidnightOpenTime = timestamp(Timezone, year, month, dayofmonth, 0, 0, 00)
CLEANUPTIME = timestamp(Timezone, year, month, dayofmonth, 0, 0, 00) - 16200000
LondonOpenTime = timestamp(Timezone, year, month, dayofmonth, 3, 0, 00)
NYOpenTime = timestamp(Timezone, year, month, dayofmonth, 8, 30, 00)
EquitiesOpenTime = timestamp(Timezone, year, month, dayofmonth, 9, 30, 00)
AfternoonOpenTime = timestamp(Timezone, year, month, dayofmonth, 13, 30, 00)
tMidnight = time("1", "0000-0001:1234567", Timezone)
// Cleanup - Remove old drawing objects
Cleanup(days) =>
// Delete old drawing objects
// One day is 86400000 milliseconds
removal_timestamp = (CLEANUPTIME) - (days * 86400000) // Remove every drawing object older than the start of the Today's Midnight
a_allLines = line.all
a_allLabels = label.all
a_allboxes = box.all
// Remove old lines
if array.size(a_allLines) > 0
for i = 0 to array.size(a_allLines) - 1
line_x2 = line.get_x2(array.get(a_allLines, i))
if line_x2 < (removal_timestamp)
line.delete(array.get(a_allLines, i))
// Remove old labels
if array.size(a_allLabels) > 0
for i = 0 to array.size(a_allLabels) - 1
label_x = label.get_x(array.get(a_allLabels, i))
if label_x < removal_timestamp
label.delete(array.get(a_allLabels, i))
// Remove old boxes
if array.size(a_allboxes) > 0
for i = 0 to array.size(a_allboxes) - 1
box_x = box.get_right(array.get(a_allboxes, i))
if box_x < (removal_timestamp - 86400000)
box.delete(array.get(a_allboxes, i))
// End of Cleanup function
// Terminus Function
Terminus(Terminus_Inp)=>
if Terminus_Inp == "Terminus @ Current Time"
_return = timenow
else if Terminus_Inp == "Terminus @ Current Time +15min"
_return = timenow + 900000
else if Terminus_Inp == "Terminus @ Current Time +30min"
_return = timenow + 1800000
else if Terminus_Inp == "Terminus @ Current Time +45min"
_return = timenow + 2700000
else if Terminus_Inp == "Terminus @ Current Time +1hr"
_return = timenow + 3600000
else if Terminus_Inp == "Terminus @ Current Time +2hr"
_return = timenow + 7200000
else
_return = timenow + 10800000
// Linestyle Function
MNOPLS = Midnight_Open_LS=="Solid" ? line.style_solid : Midnight_Open_LS == "Dotted" ? line.style_dotted : line.style_dashed
LNOPLS = london_Open_LS=="Solid" ? line.style_solid : london_Open_LS == "Dotted" ? line.style_dotted : line.style_dashed
NWYOPLS = NY_Open_LS=="Solid" ? line.style_solid : NY_Open_LS == "Dotted" ? line.style_dotted : line.style_dashed
EQOPLS = Equities_Open_LS=="Solid" ? line.style_solid : Equities_Open_LS == "Dotted" ? line.style_dotted : line.style_dashed
MOPLSS = MOPLS=="Solid" ? line.style_solid : MOPLS == "Dotted" ? line.style_dotted : line.style_dashed
NYOPLSS = NYOPLS=="Solid" ? line.style_solid : NYOPLS == "Dotted" ? line.style_dotted : line.style_dashed
EOPLSS = EOPLS=="Solid" ? line.style_solid : EOPLS == "Dotted" ? line.style_dotted : line.style_dashed
AFTOPLSS = AFTOPLS=="Solid" ? line.style_solid : AFTOPLS == "Dotted" ? line.style_dotted : line.style_dashed
WeekOpenLS = WOLS=="Solid" ? line.style_solid : WOLS == "Dotted" ? line.style_dotted : line.style_dashed
MonthOpenLS = MOLS=="Solid" ? line.style_solid : MOLS == "Dotted" ? line.style_dotted : line.style_dashed
// Linewidth Function
MOPLW = Midnight_Open_LW=="1px" ? 1 : Midnight_Open_LW == "2px" ? 2 : Midnight_Open_LW == "3px" ? 3 : Midnight_Open_LW == "4px" ? 4 : 5
LOPLW = London_Open_LW=="1px" ? 1 : London_Open_LW == "2px" ? 2 : London_Open_LW == "3px" ? 3 : London_Open_LW == "4px" ? 4 : 5
NYOPLW = NY_Open_LW=="1px" ? 1 : NY_Open_LW == "2px" ? 2 : NY_Open_LW == "3px" ? 3 : NY_Open_LW == "4px" ? 4 : 5
EOPLW = Equities_Open_LW=="1px" ? 1 : Equities_Open_LW == "2px" ? 2 : Equities_Open_LW == "3px" ? 3 : Equities_Open_LW == "4px" ? 4 : 5
MOPPLW = i_MOPLW=="1px" ? 1 : i_MOPLW == "2px" ? 2 : i_MOPLW == "3px" ? 3 : i_MOPLW == "4px" ? 4 : 5
NYOPPLW = i_NYOPLW=="1px" ? 1 : i_NYOPLW == "2px" ? 2 : i_NYOPLW == "3px" ? 3 : i_NYOPLW == "4px" ? 4 : 5
EOPPLW = i_EOPLW=="1px" ? 1 : i_EOPLW == "2px" ? 2 : i_EOPLW == "3px" ? 3 : i_EOPLW == "4px" ? 4 : 5
AFTOPLW = i_AFTOPLW=="1px" ? 1 : i_AFTOPLW == "2px" ? 2 : i_AFTOPLW == "3px" ? 3 : i_AFTOPLW == "4px" ? 4 : 5
WEEKOPPLW = i_WOPLW=="1px" ? 1 : i_WOPLW == "2px" ? 2 : i_WOPLW == "3px" ? 3 : i_WOPLW == "4px" ? 4 : 5
MONTHOPPLW = i_MONPLW=="1px" ? 1 : i_MONPLW == "2px" ? 2 : i_MONPLW == "3px" ? 3 : i_MONPLW == "4px" ? 4 : 5
// Label Size Function
LabelSize =LabelSizeInput=="Auto" ? size.auto : LabelSizeInput=="Tiny" ? size.tiny : LabelSizeInput=="Small" ? size.small : LabelSizeInput=="Normal" ? size.normal : LabelSizeInput=="Large" ? size.large : size.huge
// Creating Variables
var London_Start_Vline = line.new(x1=na, y1=na, x2=na, xloc=xloc.bar_time, y2=close, color=LSVLC, width=1)
var London_End_Vline = line.new(x1=na, y1=na, x2=na, xloc=xloc.bar_time, y2=close, color=LSVLC, width=1)
var LondonFill = linefill.new(London_Start_Vline, London_End_Vline, LSFC)
var NY_Start_Vline = line.new(x1=na, y1=na, x2=na, xloc=xloc.bar_time, y2=close, color=NYSVLC, width=1)
var NY_End_Vline = line.new(x1=na, y1=na, x2=na, xloc=xloc.bar_time, y2=close, color=NYSVLC, width=1)
var NYFill = linefill.new(NY_Start_Vline, NY_End_Vline, NYSFC)
var LC_Start_Vline = line.new(x1=na, y1=na, x2=na, xloc=xloc.bar_time, y2=close, color=NYSVLC, width=1)
var LC_End_Vline = line.new(x1=na, y1=na, x2=na, xloc=xloc.bar_time, y2=close, color=NYSVLC, width=1)
var LCFill = linefill.new(LC_Start_Vline, LC_End_Vline, LCSFC)
var PM_Start_Vline = line.new(x1=na, y1=na, x2=na, xloc=xloc.bar_time, y2=close, color=PMSVLC, width=1)
var PM_End_Vline = line.new(x1=na, y1=na, x2=na, xloc=xloc.bar_time, y2=close, color=PMSVLC, width=1)
var PMFill = linefill.new(PM_Start_Vline, PM_End_Vline, PMSFC)
var Asian_Start_Vline = line.new(x1=na, y1=na, x2=na, xloc=xloc.bar_time, y2=close, color=ASVLC, width=1)
var Asian_End_Vline = line.new(x1=na, y1=na, x2=na, xloc=xloc.bar_time, y2=close, color=ASVLC, width=1)
var AsianFill = linefill.new(Asian_Start_Vline, Asian_End_Vline, ASFC)
var Free_Start_Vline = line.new(x1=na, y1=na, x2=na, xloc=xloc.bar_time, y2=close, color=ASVLC, width=1)
var Free_End_Vline = line.new(x1=na, y1=na, x2=na, xloc=xloc.bar_time, y2=close, color=ASVLC, width=1)
var FreeFill = linefill.new(Free_Start_Vline, Free_End_Vline, FSFC)
var Midnight_Open = line.new(x1=na, y1=na, x2=na, xloc=xloc.bar_time, y2=close, color=MOPColor, width=1)
var London_Open = line.new(x1=na, y1=na, x2=na, xloc=xloc.bar_time, y2=close, color=LOPColor, width=1)
var NY_Open = line.new(x1=na, y1=na, x2=na, xloc=xloc.bar_time, y2=close, color=NYOPColor, width=1)
var Equities_Open = line.new(x1=na, y1=na, x2=na, xloc=xloc.bar_time, y2=close, color=EOPColor, width=1)
// When a New Day Starts, Start Drawing all lines
if newDay and dayofweek != dayofweek.sunday
// London Session
if (ShowLondon and DOM)
if ShowTSO
line.delete(London_Start_Vline )
line.delete(London_End_Vline )
linefill.delete(LondonFill )
London_Start_Vline := vline(LondonStartTime,transparentcol, line.style_solid, 1)
London_End_Vline := vline(LondonEndTime, transparentcol, line.style_solid, 1)
if ShowSFill
LondonFill := linefill.new(London_Start_Vline, London_End_Vline, LSFC)
// New York Session
if (ShowNY and DOM)
if ShowTSO
line.delete(NY_Start_Vline )
line.delete(NY_End_Vline )
linefill.delete(NYFill )
NY_Start_Vline := vline(NYStartTime, transparentcol, line.style_solid, 1)
NY_End_Vline := vline(NYEndTime, transparentcol, line.style_solid, 1)
if ShowSFill
NYFill := linefill.new(NY_Start_Vline, NY_End_Vline, NYSFC)
// London Close
if (ShowLC and DOM)
if ShowTSO
line.delete(LC_End_Vline )
linefill.delete(LCFill )
LC_Start_Vline := vline(LCStartTime, transparentcol, line.style_solid, 1)
LC_End_Vline := vline(LCEndTime, transparentcol, line.style_solid, 1)
if ShowSFill
LCFill := linefill.new(LC_Start_Vline, LC_End_Vline, LCSFC)
// PM Session
if (ShowPM and DOM)
if ShowTSO
line.delete(PM_Start_Vline )
line.delete(PM_End_Vline )
linefill.delete(PMFill )
PM_Start_Vline := vline(PMStartTime, transparentcol, line.style_solid, 1)
PM_End_Vline := vline(PMEndTime, transparentcol, line.style_solid, 1)
if ShowSFill
PMFill := linefill.new(PM_Start_Vline, PM_End_Vline, PMSFC)
// Asian Session
if (ShowAsian and DOM)
if ShowTSO
line.delete(Asian_Start_Vline )
line.delete(Asian_End_Vline )
linefill.delete(AsianFill )
Asian_Start_Vline := vline(AsianStartTime, transparentcol, line.style_solid, 1)
Asian_End_Vline := vline(AsianEndTime, transparentcol, line.style_solid, 1)
// if dayofweek == dayofweek.friday
// // line.delete(Asian_Start_Vline)
// // line.delete(Asian_End_Vline)
// Asian_Start_Vline := vline(MidnightOpenTime+244800000, transparentcol, line.style_solid, 1)
// Asian_End_Vline := vline(MidnightOpenTime+259200000, transparentcol, line.style_solid, 1)
if ShowSFill
AsianFill := linefill.new(Asian_Start_Vline, Asian_End_Vline, ASFC)
// Free Session
if (ShowFreeSesh and DOM)
if ShowTSO
line.delete(Free_Start_Vline )
line.delete(Free_End_Vline )
linefill.delete(FreeFill )
Free_Start_Vline := vline(FreeStartTime, transparentcol, line.style_solid, 1)
Free_End_Vline := vline(FreeEndTime, transparentcol, line.style_solid, 1)
if ShowSFill
FreeFill := linefill.new(Free_Start_Vline, Free_End_Vline, FSFC)
// Midnight Opening Price
if (ShowMOP and DOM)
if MOLHist == false
line.delete(Midnight_Open )
Midnight_Open := vline(MidnightOpenTime, MOPColor, MNOPLS, MOPLW)
// London Opening Price
if (ShowLOP and DOM)
if ShowTSO
line.delete(London_Open )
London_Open := vline(LondonOpenTime, LOPColor, LNOPLS, LOPLW)
// New York Opening Price
if (ShowNYOP and DOM)
if ShowTSO
line.delete(NY_Open )
NY_Open := vline(NYOpenTime, NYOPColor, NWYOPLS, NYOPLW)
// Equities Opening Price
if (ShowEOP and DOM)
if ShowTSO
line.delete(Equities_Open )
Equities_Open := vline(EquitiesOpenTime, EOPColor, EQOPLS, EOPLW)
// Variables
var label MOPLB = na
var line MOPLN = na
var label NYOPLB = na
var line NYOPLN = na
var label EOPLB = na
var line EOPLN = na
var line AFTLN = na
var label AFTLB = na
// New York Midnight Open Price line
var openMidnight = 0.0
if tMidnight
if not tMidnight
openMidnight := open
else
openMidnight := math.max(open, openMidnight)
if (ShowMOPP and (openMidnight != openMidnight ) and DOM and barstate.isconfirmed)
label.delete(MOPLB )
if ShowMOPL == false
line.delete(MOPLN )
MOPLN := line.new(x1=tMidnight, y1=openMidnight, x2=tMidnight+86400000, xloc=xloc.bar_time, y2=openMidnight, color=MOPColP, style=MOPLSS, width=MOPPLW)
if dayofweek == dayofweek.friday and syminfo.type != "crypto"
line.set_x2(MOPLN, tMidnight+259200000)
if ShowLabel
MOPLB := label.new(x=tMidnight+86400000, y=openMidnight, xloc=xloc.bar_time, color=LabelColor, textcolor=MOPColP, style=label.style_label_left, size=LabelSize, tooltip="Midnight Opening Price")
if dayofweek == dayofweek.friday and syminfo.type != "crypto"
label.set_x(MOPLB, tMidnight+259200000)
if ShowLabelText
if SFistrue
if ShowPrices == true
label.set_text(MOPLB, " 00:00 | " + str.tostring(open))
else
label.set_text(MOPLB, " 00:00 ")
label.set_tooltip(MOPLB, "Midnight Opening Price")
else
if ShowPrices == true
label.set_text(MOPLB, " Midnight Opening Price | " + str.tostring(open))
else
label.set_text(MOPLB, " Midnight Opening Price ")
label.set_tooltip(MOPLB, "")
label.set_textcolor(MOPLB, LabelTextColor)
label.set_size(MOPLB,LabelSize)
if time > PMEndTime and time < (MidnightOpenTime + 86400000)
line.delete(MOPLN )
if Terminusinp != "Terminus @ Next Midnight" and ShowMOPL == false
line.set_x2(MOPLN, Terminus(Terminusinp))
label.set_x(MOPLB, Terminus(Terminusinp))
// New York Opening Price Line
if (ShowNYOPP and (time == NYOpenTime) and DOM)
label.delete(NYOPLB )
if ShowPrev == false
line.delete(NYOPLN )
NYOPLN := line.new(x1=NYOpenTime, y1=open, x2=NYOpenTime+55800000, xloc=xloc.bar_time, y2=open, color=NYOPColP, style=NYOPLSS, width=NYOPPLW)
if dayofweek == dayofweek.friday and syminfo.type != "crypto"
line.set_x2(NYOPLN, NYOpenTime+228600000)
if ShowLabel
NYOPLB := label.new(x=NYOpenTime+55800000, y=open, xloc=xloc.bar_time, color=LabelColor, textcolor=NYOPColP, style=label.style_label_left, size=LabelSize, tooltip="New York Opening Price")
if dayofweek == dayofweek.friday and syminfo.type != "crypto"
label.set_x(NYOPLB, NYOpenTime+228600000)
if ShowLabelText
if SFistrue
if ShowPrices == true
label.set_text(NYOPLB, " 08:30 | " + str.tostring(open))
else
label.set_text(NYOPLB, " 08:30 ")
label.set_tooltip(NYOPLB, "New York Opening Price")
else
if ShowPrices == true
label.set_text(NYOPLB, " New York Opening Price | " + str.tostring(open))
else
label.set_text(NYOPLB, " New York Opening Price ")
label.set_tooltip(NYOPLB, "")
label.set_textcolor(NYOPLB, LabelTextColor)
label.set_size(NYOPLB,LabelSize)
if Terminusinp != "Terminus @ Next Midnight" and ShowPrev == false
line.set_x2(NYOPLN, Terminus(Terminusinp))
label.set_x(NYOPLB, Terminus(Terminusinp))
// Equities Opening Price Line
if (ShowEOPP and (time == EquitiesOpenTime) and DOM)
label.delete(EOPLB )
if ShowPrev == false
line.delete(EOPLN )
EOPLN := line.new(x1=EquitiesOpenTime, y1=open, x2=EquitiesOpenTime+52200000, xloc=xloc.bar_time, y2=open, color=EOPColP, style=EOPLSS, width=EOPPLW)
if dayofweek == dayofweek.friday and syminfo.type != "crypto"
line.set_x2(EOPLN, EquitiesOpenTime+225000000)
if ShowLabel
EOPLB := label.new(x=EquitiesOpenTime+52200000, y=open, xloc=xloc.bar_time, color=LabelColor, textcolor=EOPColP, style=label.style_label_left, size=LabelSize, tooltip="Equities Opening Price")
if dayofweek == dayofweek.friday and syminfo.type != "crypto"
label.set_x(EOPLB, EquitiesOpenTime+225000000)
if ShowLabelText
if SFistrue
if ShowPrices == true
label.set_text(EOPLB, " 09:30 | " + str.tostring(open))
else
label.set_text(EOPLB, " 09:30 ")
label.set_tooltip(EOPLB, "Equities Opening Price")
else
if ShowPrices == true
label.set_text(EOPLB, " Equities Opening Price | " + str.tostring(open))
else
label.set_text(EOPLB, " Equities Opening Price ")
label.set_tooltip(EOPLB, "")
label.set_textcolor(EOPLB, LabelTextColor)
label.set_size(EOPLB,LabelSize)
if Terminusinp != "Terminus @ Next Midnight" and ShowPrev == false
line.set_x2(EOPLN, Terminus(Terminusinp))
label.set_x(EOPLB, Terminus(Terminusinp))
// Afternoon Opening Price Line
if (ShowAFTPP and (time == AfternoonOpenTime) and DOM)
label.delete(AFTLB )
if ShowPrev == false
line.delete(AFTLN )
AFTLN := line.new(x1=AfternoonOpenTime, y1=open, x2=EquitiesOpenTime+52200000, xloc=xloc.bar_time, y2=open, color=AFTOPColP, style=AFTOPLSS, width=AFTOPLW)
if dayofweek == dayofweek.friday and syminfo.type != "crypto"
line.set_x2(AFTLN, EquitiesOpenTime+225000000)
if ShowLabel
AFTLB := label.new(x=EquitiesOpenTime+52200000, y=open, xloc=xloc.bar_time, color=LabelColor, textcolor=AFTOPColP, style=label.style_label_left, size=LabelSize, tooltip="Equities Opening Price")
if dayofweek == dayofweek.friday and syminfo.type != "crypto"
label.set_x(AFTLB, EquitiesOpenTime+225000000)
if ShowLabelText
if SFistrue
if ShowPrices == true
label.set_text(AFTLB, " 01:30 | " + str.tostring(open))
else
label.set_text(AFTLB, " 01:30 ")
label.set_tooltip(AFTLB, " Afternoon Opening Price")
else
if ShowPrices == true
label.set_text(AFTLB, " Afternoon Opening Price | " + str.tostring(open))
else
label.set_text(AFTLB, " Afternoon Opening Price ")
label.set_tooltip(AFTLB, "")
label.set_textcolor(AFTLB, LabelTextColor)
label.set_size(AFTLB,LabelSize)
if Terminusinp != "Terminus @ Next Midnight" and ShowPrev == false
line.set_x2(AFTLN, Terminus(Terminusinp))
label.set_x(AFTLB, Terminus(Terminusinp))
// HTF Variables
var Weekly_open = line.new(x1=na, y1=na, x2=na, xloc=xloc.bar_time, y2=close, color=i_WeekOpenCol, style=WeekOpenLS, width=1)
var Weekly_openlbl = label.new(x=na, y=na, xloc=xloc.bar_time, color=LabelColor, textcolor=LabelTextColor, style=label.style_label_left, size=LabelSize)
var WeeklyOpenTime = time
var Monthly_open = line.new(x1=na, y1=na, x2=na, xloc=xloc.bar_time, y2=close, color=i_MonthOpenCol, style=MonthOpenLS, width=1)
var Monthly_openlbl = label.new(x=na, y=na, xloc=xloc.bar_time, color=LabelColor, textcolor=LabelTextColor, style=label.style_label_left, size=LabelSize)
var MonthlyOpenTime = time
// Get HTF Price levels
WeeklyOpen = request.security(syminfo.tickerid, "W", open, lookahead = barmerge.lookahead_on)
MonthlyOpen = request.security(syminfo.tickerid, "M", open, lookahead = barmerge.lookahead_on)
// Weekly Open
if newWeek
WeeklyOpenTime := time
if ShowWeekOpen and newDay and Last4Weeks
label.delete(Weekly_openlbl )
line.delete(Weekly_open )
// if ShowPrev == false
// line.delete(Weekly_open )
Weekly_open:= line.new(x1=WeeklyOpenTime-25200000, y1=WeeklyOpen, x2=EquitiesOpenTime+52200000, xloc=xloc.bar_time, y2=WeeklyOpen, color=i_WeekOpenCol, style=WeekOpenLS, width=WEEKOPPLW)
if dayofweek == dayofweek.friday and syminfo.type != "crypto"
line.set_x2(Weekly_open, EquitiesOpenTime+225000000)
if ShowLabel
Weekly_openlbl := label.new(x=EquitiesOpenTime+52200000, y=WeeklyOpen, xloc=xloc.bar_time, color=LabelColor, textcolor=LabelTextColor, style=label.style_label_left, size=LabelSize, tooltip="Weekly Open: " + str.tostring(WeeklyOpen))
if dayofweek == dayofweek.friday and syminfo.type != "crypto"
label.set_x(Weekly_openlbl, EquitiesOpenTime+225000000)
if ShowLabelText
if SFistrue
if ShowPrices == true
label.set_text(Weekly_openlbl," W.O. | " + str.tostring(WeeklyOpen))
else
label.set_text(Weekly_openlbl," W.O. ")
label.set_tooltip(Weekly_openlbl, " Weekly Opening Price ")
else
if ShowPrices == true
label.set_text(Weekly_openlbl," Weekly Open | " + str.tostring(WeeklyOpen))
else
label.set_text(Weekly_openlbl," Weekly Open ")
label.set_tooltip(Weekly_openlbl, "")
label.set_textcolor(Weekly_openlbl, LabelTextColor)
label.set_size(Weekly_openlbl, LabelSize)
if timeframe.multiplier > 60
line.set_x2(Weekly_open, AsianEndTime + 232000000)
label.set_x(Weekly_openlbl, AsianEndTime + 232000000)
if timeframe.period == "D"
line.set_x2(Weekly_open, AsianEndTime + 832000000)
label.set_x(Weekly_openlbl, AsianEndTime + 832000000)
if timeframe.period == "M"
line.delete(Weekly_open)
label.delete(Weekly_openlbl)
if Terminusinp != "Terminus @ Next Midnight" and DOM
line.set_x2(Weekly_open, Terminus(Terminusinp))
label.set_x(Weekly_openlbl, Terminus(Terminusinp))
// Monthly Open
if newMonth
MonthlyOpenTime := time
if showMonthOpen and newDay
line.delete(Monthly_open )
label.delete(Monthly_openlbl )
Monthly_open:= line.new(x1=MonthlyOpenTime, y1=MonthlyOpen, x2=AsianEndTime, xloc=xloc.bar_time, y2=MonthlyOpen, color=i_MonthOpenCol, style=MonthOpenLS, width=MONTHOPPLW)
if dayofweek == dayofweek.friday and syminfo.type != "crypto"
line.set_x2(Monthly_open, EquitiesOpenTime+225000000)
if ShowLabel
Monthly_openlbl := label.new(x=AsianEndTime, y=MonthlyOpen, xloc=xloc.bar_time, color=LabelColor, textcolor=LabelTextColor, style=label.style_label_left, size=LabelSize, tooltip="Monthly Open: " + str.tostring(MonthlyOpen))
if dayofweek == dayofweek.friday and syminfo.type != "crypto"
label.set_x(Monthly_openlbl, EquitiesOpenTime+225000000)
if ShowLabelText
if SFistrue
if ShowPrices == true
label.set_text(Monthly_openlbl," M.O. | " + str.tostring(MonthlyOpen))
else
label.set_text(Monthly_openlbl," M.O. ")
label.set_tooltip(Monthly_openlbl, " Monthly Opening Price ")
else
if ShowPrices == true
label.set_text(Monthly_openlbl, " Monthly Open | " + str.tostring(MonthlyOpen))
else
label.set_text(Monthly_openlbl, " Monthly Open ")
label.set_tooltip(Monthly_openlbl, "")
label.set_textcolor(Monthly_openlbl, LabelTextColor)
label.set_size(Monthly_openlbl, LabelSize)
if timeframe.multiplier > 60
line.set_x2(Monthly_open, AsianEndTime + 232000000)
label.set_x(Monthly_openlbl, AsianEndTime + 232000000)
if timeframe.period == "D"
line.set_x2(Monthly_open, AsianEndTime + 832000000)
label.set_x(Monthly_openlbl, AsianEndTime + 832000000)
if timeframe.period == "W"
line.set_x2(Monthly_open, AsianEndTime + 2592000000)
label.set_x(Monthly_openlbl, AsianEndTime + 2592000000)
if timeframe.period == "M"
line.delete(Monthly_open)
label.delete(Monthly_openlbl)
if Terminusinp != "Terminus @ Next Midnight" and DOM
line.set_x2(Monthly_open, Terminus(Terminusinp))
label.set_x(Monthly_openlbl, Terminus(Terminusinp))
// CBDR Stuff
var float cbdr_hi = na
var float cbdr_lo = na
var float cbdr_diff = na
var box cbdrbox = na
var line cbdr_hi_line = na
var line cbdr_lo_line = na
var line dev01negline = na
var line dev02negline = na
var line dev03negline = na
var line dev04negline = na
var line dev01posline = na
var line dev02posline = na
var line dev03posline = na
var line dev04posline = na
if SessionBegins(CBDR) and DOM
cbdr_hi := high
cbdr_lo := low
cbdr_diff := cbdr_hi - cbdr_lo
if ShowTSO
box.delete(cbdrbox )
line.delete(dev01posline )
line.delete(dev01negline )
line.delete(dev02posline )
line.delete(dev02negline )
line.delete(dev03posline )
line.delete(dev03negline )
line.delete(dev04posline )
line.delete(dev04negline )
if ShowCBDR
cbdrbox := box.new(cbdrOpenTime, cbdr_hi, cbdrEndTime, cbdr_lo, color.new(CBDRBoxCol,90), 1, line.style_solid, extend.none, xloc.bar_time, color.new(CBDRBoxCol,90), txt0, size.auto, color.new(box_text_cbdr_col,80), text_wrap=text.wrap_auto)
if dayofweek == dayofweek.friday
box.set_right(cbdrbox, cbdrOpenTime+187200000)
line.set_x2(cbdr_hi_line, cbdrOpenTime+187200000)
line.set_x2(cbdr_lo_line, cbdrOpenTime+187200000)
if box_text_cbdr == false
box.set_text(cbdrbox, "")
if ShowDev and ShowCBDR and bool_cbdr_dev
for i = 1 to DevCount by 1
if i == 1
dev01posline := line.new(cbdrOpenTime, cbdr_hi + cbdr_diff * i, cbdrEndTime, cbdr_hi + cbdr_diff * i, xloc=xloc.bar_time, color=DevLNCol, style=DEVLSS, width=DEVLW)
dev01negline := line.new(cbdrOpenTime, cbdr_hi - cbdr_diff * i, cbdrEndTime, cbdr_lo - cbdr_diff * i, xloc=xloc.bar_time, color=DevLNCol, style=DEVLSS, width=DEVLW)
if dayofweek == dayofweek.friday
line.set_x2(dev01posline, cbdrOpenTime+187200000)
line.set_x2(dev01negline, cbdrOpenTime+187200000)
if i == 2
dev02posline := line.new(cbdrOpenTime, cbdr_hi + cbdr_diff * i, cbdrEndTime, cbdr_lo + cbdr_diff * i, xloc=xloc.bar_time, color=DevLNCol, style=DEVLSS, width=DEVLW)
dev02negline := line.new(cbdrOpenTime, cbdr_hi - cbdr_diff * i, cbdrEndTime, cbdr_lo - cbdr_diff * i, xloc=xloc.bar_time, color=DevLNCol, style=DEVLSS, width=DEVLW)
if dayofweek == dayofweek.friday
line.set_x2(dev02posline, cbdrOpenTime+187200000)
line.set_x2(dev02negline, cbdrOpenTime+187200000)
if i == 3
dev03posline := line.new(cbdrOpenTime, cbdr_hi + cbdr_diff * i, cbdrEndTime, cbdr_lo + cbdr_diff * i, xloc=xloc.bar_time, color=DevLNCol, style=DEVLSS, width=DEVLW)
dev03negline := line.new(cbdrOpenTime, cbdr_hi - cbdr_diff * i, cbdrEndTime, cbdr_lo - cbdr_diff * i, xloc=xloc.bar_time, color=DevLNCol, style=DEVLSS, width=DEVLW)
if dayofweek == dayofweek.friday
line.set_x2(dev03posline, cbdrOpenTime+187200000)
line.set_x2(dev03negline, cbdrOpenTime+187200000)
if i == 4
dev04posline := line.new(cbdrOpenTime, cbdr_hi + cbdr_diff * i, cbdrEndTime, cbdr_lo + cbdr_diff * i, xloc=xloc.bar_time, color=DevLNCol, style=DEVLSS, width=DEVLW)
dev04negline := line.new(cbdrOpenTime, cbdr_hi - cbdr_diff * i, cbdrEndTime, cbdr_lo - cbdr_diff * i, xloc=xloc.bar_time, color=DevLNCol, style=DEVLSS, width=DEVLW)
if dayofweek == dayofweek.friday
line.set_x2(dev04posline, cbdrOpenTime+187200000)
line.set_x2(dev04negline, cbdrOpenTime+187200000)
else if CBDRTime
cbdr_hi := math.max(high, cbdr_hi)
cbdr_lo := math.min(low, cbdr_lo)
cbdr_diff := cbdr_hi - cbdr_lo
for i = 1 to DevCount by 1
if i == 1 and ShowDev
line.set_y1(dev01posline, cbdr_hi + cbdr_diff * i)
line.set_y2(dev01posline, cbdr_hi + cbdr_diff * i)
line.set_y1(dev01negline, cbdr_lo - cbdr_diff * i)
line.set_y2(dev01negline, cbdr_lo - cbdr_diff * i)
if i == 2 and ShowDev
line.set_y1(dev02posline, cbdr_hi + cbdr_diff * i)
line.set_y2(dev02posline, cbdr_hi + cbdr_diff * i)
line.set_y1(dev02negline, cbdr_lo - cbdr_diff * i)
line.set_y2(dev02negline, cbdr_lo - cbdr_diff * i)
if i == 3 and ShowDev
line.set_y1(dev03posline, cbdr_hi + cbdr_diff * i)
line.set_y2(dev03posline, cbdr_hi + cbdr_diff * i)
line.set_y1(dev03negline, cbdr_lo - cbdr_diff * i)
line.set_y2(dev03negline, cbdr_lo - cbdr_diff * i)
if i == 4 and ShowDev
line.set_y1(dev04posline, cbdr_hi + cbdr_diff * i)
line.set_y2(dev04posline, cbdr_hi + cbdr_diff * i)
line.set_y1(dev04negline, cbdr_lo - cbdr_diff * i)
line.set_y2(dev04negline, cbdr_lo - cbdr_diff * i)
if (cbdr_hi > cbdr_hi )
if ShowCBDR
box.set_top(cbdrbox, cbdr_hi)
if (cbdr_lo < cbdr_lo )
if ShowCBDR
box.set_bottom(cbdrbox, cbdr_lo)
if DevDirection == "Upside Only"
line.delete(dev01negline)
line.delete(dev02negline)
line.delete(dev03negline)
line.delete(dev04negline)
else if DevDirection == "Downside Only"
line.delete(dev01posline)
line.delete(dev02posline)
line.delete(dev03posline)
line.delete(dev04posline)
// ASIA Stuff
var float asia_hi = na
var float asia_lo = na
var float asia_diff = na
var box asia_box = na
var line asia_hi_line = na
var line asia_lo_line = na
var line dev01negline_asia = na
var line dev02negline_asia = na
var line dev03negline_asia = na
var line dev04negline_asia = na
var line dev01posline_asia = na
var line dev02posline_asia = na
var line dev03posline_asia = na
var line dev04posline_asia = na
if SessionBegins(ASIA) and DOM
asia_hi := high
asia_lo := low
asia_diff := asia_hi - asia_lo
if ShowTSO
box.delete(asia_box )
line.delete(dev01posline_asia )
line.delete(dev01negline_asia )
line.delete(dev02posline_asia )
line.delete(dev02negline_asia )
line.delete(dev03posline_asia )
line.delete(dev03negline_asia )
line.delete(dev04posline_asia )
line.delete(dev04negline_asia )
if ShowASIA
asia_box := box.new(asiaOpenTime, asia_hi, asiaEndTime, asia_lo, color.new(ASIABoxCol,90), 1, line.style_solid, extend.none, xloc.bar_time, color.new(ASIABoxCol,90), txt1, size.auto, color.new(box_text_asia_col,80), text_wrap=text.wrap_auto)
if box_text_asia == false
box.set_text(asia_box, "")
if ShowDev and ShowASIA and bool_asia_dev
for i = 1 to DevCount by 1
if i == 1
dev01posline_asia := line.new(asiaOpenTime, asia_hi + asia_diff * i, asiaEndTime, asia_hi + asia_diff * i, xloc=xloc.bar_time, color=DevLNCol, style=DEVLSS, width=DEVLW)
dev01negline_asia := line.new(asiaOpenTime, asia_hi - asia_diff * i, asiaEndTime, asia_lo - asia_diff * i, xloc=xloc.bar_time, color=DevLNCol, style=DEVLSS, width=DEVLW)
if i == 2
dev02posline_asia := line.new(asiaOpenTime, asia_hi + asia_diff * i, asiaEndTime, asia_lo + asia_diff * i, xloc=xloc.bar_time, color=DevLNCol, style=DEVLSS, width=DEVLW)
dev02negline_asia := line.new(asiaOpenTime, asia_hi - asia_diff * i, asiaEndTime, asia_lo - asia_diff * i, xloc=xloc.bar_time, color=DevLNCol, style=DEVLSS, width=DEVLW)
if i == 3
dev03posline_asia := line.new(asiaOpenTime, asia_hi + asia_diff * i, asiaEndTime, asia_lo + asia_diff * i, xloc=xloc.bar_time, color=DevLNCol, style=DEVLSS, width=DEVLW)
dev03negline_asia := line.new(asiaOpenTime, asia_hi - asia_diff * i, asiaEndTime, asia_lo - asia_diff * i, xloc=xloc.bar_time, color=DevLNCol, style=DEVLSS, width=DEVLW)
if i == 4
dev04posline_asia := line.new(asiaOpenTime, asia_hi + asia_diff * i, asiaEndTime, asia_lo + asia_diff * i, xloc=xloc.bar_time, color=DevLNCol, style=DEVLSS, width=DEVLW)
dev04negline_asia := line.new(asiaOpenTime, asia_hi - asia_diff * i, asiaEndTime, asia_lo - asia_diff * i, xloc=xloc.bar_time, color=DevLNCol, style=DEVLSS, width=DEVLW)
else if ASIATime
asia_hi := math.max(high, asia_hi)
asia_lo := math.min(low, asia_lo)
asia_diff := asia_hi - asia_lo
for i = 1 to DevCount by 1
if i == 1 and ShowDev
line.set_y1(dev01posline_asia, asia_hi + asia_diff * i)
line.set_y2(dev01posline_asia, asia_hi + asia_diff * i)
line.set_y1(dev01negline_asia, asia_lo - asia_diff * i)
line.set_y2(dev01negline_asia, asia_lo - asia_diff * i)
if i == 2 and ShowDev
line.set_y1(dev02posline_asia, asia_hi + asia_diff * i)
line.set_y2(dev02posline_asia, asia_hi + asia_diff * i)
line.set_y1(dev02negline_asia, asia_lo - asia_diff * i)
line.set_y2(dev02negline_asia, asia_lo - asia_diff * i)
if i == 3 and ShowDev
line.set_y1(dev03posline_asia, asia_hi + asia_diff * i)
line.set_y2(dev03posline_asia, asia_hi + asia_diff * i)
line.set_y1(dev03negline_asia, asia_lo - asia_diff * i)
line.set_y2(dev03negline_asia, asia_lo - asia_diff * i)
if i == 4 and ShowDev
line.set_y1(dev04posline_asia, asia_hi + asia_diff * i)
line.set_y2(dev04posline_asia, asia_hi + asia_diff * i)
line.set_y1(dev04negline_asia, asia_lo - asia_diff * i)
line.set_y2(dev04negline_asia, asia_lo - asia_diff * i)
if (asia_hi > asia_hi )
box.set_top(asia_box, asia_hi)
if (asia_lo < asia_lo )
box.set_bottom(asia_box, asia_lo)
if DevDirection == "Upside Only"
line.delete(dev01negline_asia)
line.delete(dev02negline_asia)
line.delete(dev03negline_asia)
line.delete(dev04negline_asia)
else if DevDirection == "Downside Only"
line.delete(dev01posline_asia)
line.delete(dev02posline_asia)
line.delete(dev03posline_asia)
line.delete(dev04posline_asia)
// FLOUT Stuff
var float flout_hi = na
var float flout_lo = na
var float flout_diff = na
var box floutbox = na
var line flout_hi_line = na
var line flout_lo_line = na
var line dev01negline_flout = na
var line dev02negline_flout = na
var line dev03negline_flout = na
var line dev04negline_flout = na
var line dev01posline_flout = na
var line dev02posline_flout = na
var line dev03posline_flout = na
var line dev04posline_flout = na
if SessionBegins(FLOUT) and DOM
flout_hi := high
flout_lo := low
flout_diff := flout_hi - flout_lo
if ShowTSO
box.delete(floutbox )
line.delete(dev01posline_flout )
line.delete(dev01negline_flout )
line.delete(dev02posline_flout )
line.delete(dev02negline_flout )
line.delete(dev03posline_flout )
line.delete(dev03negline_flout )
line.delete(dev04posline_flout )
line.delete(dev04negline_flout )
if ShowFLOUT
floutbox := box.new(floutOpenTime, flout_hi, floutEndTime, flout_lo, color.new(FLOUTBoxCol,90), 1, line.style_solid, extend.none, xloc.bar_time, color.new(FLOUTBoxCol,90), txt7, size.auto, color.new(box_text_flout_col,80), text_wrap=text.wrap_auto)
if dayofweek == dayofweek.friday
box.set_right(floutbox, floutOpenTime+201600000)
line.set_x2(flout_hi_line, floutOpenTime+201600000)
line.set_x2(flout_lo_line, floutOpenTime+201600000)
if box_text_cbdr == false
box.set_text(floutbox, "")
if ShowDev and ShowFLOUT and bool_flout_dev
for i = 0.5 to DevCount by 0.5
if i == 0.5
dev01posline_flout := line.new(floutOpenTime, flout_hi + flout_diff * i, floutEndTime, flout_hi + flout_diff * i, xloc=xloc.bar_time, color=DevLNCol, style=DEVLSS, width=DEVLW)
dev01negline_flout := line.new(floutOpenTime, flout_hi - flout_diff * i, floutEndTime, flout_lo - flout_diff * i, xloc=xloc.bar_time, color=DevLNCol, style=DEVLSS, width=DEVLW)
if dayofweek == dayofweek.friday
line.set_x2(dev01posline_flout, floutOpenTime+201600000)
line.set_x2(dev01negline_flout, floutOpenTime+201600000)
if i == 1
dev02posline_flout := line.new(floutOpenTime, flout_hi + flout_diff * i, floutEndTime, flout_lo + flout_diff * i, xloc=xloc.bar_time, color=DevLNCol, style=DEVLSS, width=DEVLW)
dev02negline_flout := line.new(floutOpenTime, flout_hi - flout_diff * i, floutEndTime, flout_lo - flout_diff * i, xloc=xloc.bar_time, color=DevLNCol, style=DEVLSS, width=DEVLW)
if dayofweek == dayofweek.friday
line.set_x2(dev02posline_flout, floutOpenTime+201600000)
line.set_x2(dev02negline_flout, floutOpenTime+201600000)
if i == 1.5
dev03posline_flout := line.new(floutOpenTime, flout_hi + flout_diff * i, floutEndTime, flout_lo + flout_diff * i, xloc=xloc.bar_time, color=DevLNCol, style=DEVLSS, width=DEVLW)
dev03negline_flout := line.new(floutOpenTime, flout_hi - flout_diff * i, floutEndTime, flout_lo - flout_diff * i, xloc=xloc.bar_time, color=DevLNCol, style=DEVLSS, width=DEVLW)
if dayofweek == dayofweek.friday
line.set_x2(dev03posline_flout, floutOpenTime+201600000)
line.set_x2(dev03negline_flout, floutOpenTime+201600000)
if i == 2
dev04posline_flout := line.new(floutOpenTime, flout_hi + flout_diff * i, floutEndTime, flout_lo + flout_diff * i, xloc=xloc.bar_time, color=DevLNCol, style=DEVLSS, width=DEVLW)
dev04negline_flout := line.new(floutOpenTime, flout_hi - flout_diff * i, floutEndTime, flout_lo - flout_diff * i, xloc=xloc.bar_time, color=DevLNCol, style=DEVLSS, width=DEVLW)
if dayofweek == dayofweek.friday
line.set_x2(dev04posline_flout, floutOpenTime+201600000)
line.set_x2(dev04negline_flout, floutOpenTime+201600000)
else if FLOUTTime
flout_hi := math.max(high, flout_hi)
flout_lo := math.min(low, flout_lo)
flout_diff := flout_hi - flout_lo
for i = 0.5 to DevCount by 0.5
if i == 0.5 and ShowDev
line.set_y1(dev01posline_flout, flout_hi + flout_diff * i)
line.set_y2(dev01posline_flout, flout_hi + flout_diff * i)
line.set_y1(dev01negline_flout, flout_lo - flout_diff * i)
line.set_y2(dev01negline_flout, flout_lo - flout_diff * i)
if i == 1 and ShowDev
line.set_y1(dev02posline_flout, flout_hi + flout_diff * i)
line.set_y2(
Kelly Position Size CalculatorThis position sizing calculator implements the Kelly Criterion, developed by John L. Kelly Jr. at Bell Laboratories in 1956, to determine mathematically optimal position sizes for maximizing long-term wealth growth. Unlike arbitrary position sizing methods, this tool provides a scientifically solution based on your strategy's actual performance statistics and incorporates modern refinements from over six decades of academic research.
The Kelly Criterion addresses a fundamental question in capital allocation: "What fraction of capital should be allocated to each opportunity to maximize growth while avoiding ruin?" This question has profound implications for financial markets, where traders and investors constantly face decisions about optimal capital allocation (Van Tharp, 2007).
Theoretical Foundation
The Kelly Criterion for binary outcomes is expressed as f* = (bp - q) / b, where f* represents the optimal fraction of capital to allocate, b denotes the risk-reward ratio, p indicates the probability of success, and q represents the probability of loss (Kelly, 1956). This formula maximizes the expected logarithm of wealth, ensuring maximum long-term growth rate while avoiding the risk of ruin.
The mathematical elegance of Kelly's approach lies in its derivation from information theory. Kelly's original work was motivated by Claude Shannon's information theory (Shannon, 1948), recognizing that maximizing the logarithm of wealth is equivalent to maximizing the rate of information transmission. This connection between information theory and wealth accumulation provides a deep theoretical foundation for optimal position sizing.
The logarithmic utility function underlying the Kelly Criterion naturally embodies several desirable properties for capital management. It exhibits decreasing marginal utility, penalizes large losses more severely than it rewards equivalent gains, and focuses on geometric rather than arithmetic mean returns, which is appropriate for compounding scenarios (Thorp, 2006).
Scientific Implementation
This calculator extends beyond basic Kelly implementation by incorporating state of the art refinements from academic research:
Parameter Uncertainty Adjustment: Following Michaud (1989), the implementation applies Bayesian shrinkage to account for parameter estimation error inherent in small sample sizes. The adjustment formula f_adjusted = f_kelly × confidence_factor + f_conservative × (1 - confidence_factor) addresses the overconfidence bias documented by Baker and McHale (2012), where the confidence factor increases with sample size and the conservative estimate equals 0.25 (quarter Kelly).
Sample Size Confidence: The reliability of Kelly calculations depends critically on sample size. Research by Browne and Whitt (1996) provides theoretical guidance on minimum sample requirements, suggesting that at least 30 independent observations are necessary for meaningful parameter estimates, with 100 or more trades providing reliable estimates for most trading strategies.
Universal Asset Compatibility: The calculator employs intelligent asset detection using TradingView's built-in symbol information, automatically adapting calculations for different asset classes without manual configuration.
ASSET SPECIFIC IMPLEMENTATION
Equity Markets: For stocks and ETFs, position sizing follows the calculation Shares = floor(Kelly Fraction × Account Size / Share Price). This straightforward approach reflects whole share constraints while accommodating fractional share trading capabilities.
Foreign Exchange Markets: Forex markets require lot-based calculations following Lot Size = Kelly Fraction × Account Size / (100,000 × Base Currency Value). The calculator automatically handles major currency pairs with appropriate pip value calculations, following industry standards described by Archer (2010).
Futures Markets: Futures position sizing accounts for leverage and margin requirements through Contracts = floor(Kelly Fraction × Account Size / Margin Requirement). The calculator estimates margin requirements as a percentage of contract notional value, with specific adjustments for micro-futures contracts that have smaller sizes and reduced margin requirements (Kaufman, 2013).
Index and Commodity Markets: These markets combine characteristics of both equity and futures markets. The calculator automatically detects whether instruments are cash-settled or futures-based, applying appropriate sizing methodologies with correct point value calculations.
Risk Management Integration
The calculator integrates sophisticated risk assessment through two primary modes:
Stop Loss Integration: When fixed stop-loss levels are defined, risk calculation follows Risk per Trade = Position Size × Stop Loss Distance. This ensures that the Kelly fraction accounts for actual risk exposure rather than theoretical maximum loss, with stop-loss distance measured in appropriate units for each asset class.
Strategy Drawdown Assessment: For discretionary exit strategies, risk estimation uses maximum historical drawdown through Risk per Trade = Position Value × (Maximum Drawdown / 100). This approach assumes that individual trade losses will not exceed the strategy's historical maximum drawdown, providing a reasonable estimate for strategies with well-defined risk characteristics.
Fractional Kelly Approaches
Pure Kelly sizing can produce substantial volatility, leading many practitioners to adopt fractional Kelly approaches. MacLean, Sanegre, Zhao, and Ziemba (2004) analyze the trade-offs between growth rate and volatility, demonstrating that half-Kelly typically reduces volatility by approximately 75% while sacrificing only 25% of the growth rate.
The calculator provides three primary Kelly modes to accommodate different risk preferences and experience levels. Full Kelly maximizes growth rate while accepting higher volatility, making it suitable for experienced practitioners with strong risk tolerance and robust capital bases. Half Kelly offers a balanced approach popular among professional traders, providing optimal risk-return balance by reducing volatility significantly while maintaining substantial growth potential. Quarter Kelly implements a conservative approach with low volatility, recommended for risk-averse traders or those new to Kelly methodology who prefer gradual introduction to optimal position sizing principles.
Empirical Validation and Performance
Extensive academic research supports the theoretical advantages of Kelly sizing. Hakansson and Ziemba (1995) provide a comprehensive review of Kelly applications in finance, documenting superior long-term performance across various market conditions and asset classes. Estrada (2008) analyzes Kelly performance in international equity markets, finding that Kelly-based strategies consistently outperform fixed position sizing approaches over extended periods across 19 developed markets over a 30-year period.
Several prominent investment firms have successfully implemented Kelly-based position sizing. Pabrai (2007) documents the application of Kelly principles at Berkshire Hathaway, noting Warren Buffett's concentrated portfolio approach aligns closely with Kelly optimal sizing for high-conviction investments. Quantitative hedge funds, including Renaissance Technologies and AQR, have incorporated Kelly-based risk management into their systematic trading strategies.
Practical Implementation Guidelines
Successful Kelly implementation requires systematic application with attention to several critical factors:
Parameter Estimation: Accurate parameter estimation represents the greatest challenge in practical Kelly implementation. Brown (1976) notes that small errors in probability estimates can lead to significant deviations from optimal performance. The calculator addresses this through Bayesian adjustments and confidence measures.
Sample Size Requirements: Users should begin with conservative fractional Kelly approaches until achieving sufficient historical data. Strategies with fewer than 30 trades may produce unreliable Kelly estimates, regardless of adjustments. Full confidence typically requires 100 or more independent trade observations.
Market Regime Considerations: Parameters that accurately describe historical performance may not reflect future market conditions. Ziemba (2003) recommends regular parameter updates and conservative adjustments when market conditions change significantly.
Professional Features and Customization
The calculator provides comprehensive customization options for professional applications:
Multiple Color Schemes: Eight professional color themes (Gold, EdgeTools, Behavioral, Quant, Ocean, Fire, Matrix, Arctic) with dark and light theme compatibility ensure optimal visibility across different trading environments.
Flexible Display Options: Adjustable table size and position accommodate various chart layouts and user preferences, while maintaining analytical depth and clarity.
Comprehensive Results: The results table presents essential information including asset specifications, strategy statistics, Kelly calculations, sample confidence measures, position values, risk assessments, and final position sizes in appropriate units for each asset class.
Limitations and Considerations
Like any analytical tool, the Kelly Criterion has important limitations that users must understand:
Stationarity Assumption: The Kelly Criterion assumes that historical strategy statistics represent future performance characteristics. Non-stationary market conditions may invalidate this assumption, as noted by Lo and MacKinlay (1999).
Independence Requirement: Each trade should be independent to avoid correlation effects. Many trading strategies exhibit serial correlation in returns, which can affect optimal position sizing and may require adjustments for portfolio applications.
Parameter Sensitivity: Kelly calculations are sensitive to parameter accuracy. Regular calibration and conservative approaches are essential when parameter uncertainty is high.
Transaction Costs: The implementation incorporates user-defined transaction costs but assumes these remain constant across different position sizes and market conditions, following Ziemba (2003).
Advanced Applications and Extensions
Multi-Asset Portfolio Considerations: While this calculator optimizes individual position sizes, portfolio-level applications require additional considerations for correlation effects and aggregate risk management. Simplified portfolio approaches include treating positions independently with correlation adjustments.
Behavioral Factors: Behavioral finance research reveals systematic biases that can interfere with Kelly implementation. Kahneman and Tversky (1979) document loss aversion, overconfidence, and other cognitive biases that lead traders to deviate from optimal strategies. Successful implementation requires disciplined adherence to calculated recommendations.
Time-Varying Parameters: Advanced implementations may incorporate time-varying parameter models that adjust Kelly recommendations based on changing market conditions, though these require sophisticated econometric techniques and substantial computational resources.
Comprehensive Usage Instructions and Practical Examples
Implementation begins with loading the calculator on your desired trading instrument's chart. The system automatically detects asset type across stocks, forex, futures, and cryptocurrency markets while extracting current price information. Navigation to the indicator settings allows input of your specific strategy parameters.
Strategy statistics configuration requires careful attention to several key metrics. The win rate should be calculated from your backtest results using the formula of winning trades divided by total trades multiplied by 100. Average win represents the sum of all profitable trades divided by the number of winning trades, while average loss calculates the sum of all losing trades divided by the number of losing trades, entered as a positive number. The total historical trades parameter requires the complete number of trades in your backtest, with a minimum of 30 trades recommended for basic functionality and 100 or more trades optimal for statistical reliability. Account size should reflect your available trading capital, specifically the risk capital allocated for trading rather than total net worth.
Risk management configuration adapts to your specific trading approach. The stop loss setting should be enabled if you employ fixed stop-loss exits, with the stop loss distance specified in appropriate units depending on the asset class. For stocks, this distance is measured in dollars, for forex in pips, and for futures in ticks. When stop losses are not used, the maximum strategy drawdown percentage from your backtest provides the risk assessment baseline. Kelly mode selection offers three primary approaches: Full Kelly for aggressive growth with higher volatility suitable for experienced practitioners, Half Kelly for balanced risk-return optimization popular among professional traders, and Quarter Kelly for conservative approaches with reduced volatility.
Display customization ensures optimal integration with your trading environment. Eight professional color themes provide optimization for different chart backgrounds and personal preferences. Table position selection allows optimal placement within your chart layout, while table size adjustment ensures readability across different screen resolutions and viewing preferences.
Detailed Practical Examples
Example 1: SPY Swing Trading Strategy
Consider a professionally developed swing trading strategy for SPY (S&P 500 ETF) with backtesting results spanning 166 total trades. The strategy achieved 110 winning trades, representing a 66.3% win rate, with an average winning trade of $2,200 and average losing trade of $862. The maximum drawdown reached 31.4% during the testing period, and the available trading capital amounts to $25,000. This strategy employs discretionary exits without fixed stop losses.
Implementation requires loading the calculator on the SPY daily chart and configuring the parameters accordingly. The win rate input receives 66.3, while average win and loss inputs receive 2200 and 862 respectively. Total historical trades input requires 166, with account size set to 25000. The stop loss function remains disabled due to the discretionary exit approach, with maximum strategy drawdown set to 31.4%. Half Kelly mode provides the optimal balance between growth and risk management for this application.
The calculator generates several key outputs for this scenario. The risk-reward ratio calculates automatically to 2.55, while the Kelly fraction reaches approximately 53% before scientific adjustments. Sample confidence achieves 100% given the 166 trades providing high statistical confidence. The recommended position settles at approximately 27% after Half Kelly and Bayesian adjustment factors. Position value reaches approximately $6,750, translating to 16 shares at a $420 SPY price. Risk per trade amounts to approximately $2,110, representing 31.4% of position value, with expected value per trade reaching approximately $1,466. This recommendation represents the mathematically optimal balance between growth potential and risk management for this specific strategy profile.
Example 2: EURUSD Day Trading with Stop Losses
A high-frequency EURUSD day trading strategy demonstrates different parameter requirements compared to swing trading approaches. This strategy encompasses 89 total trades with a 58% win rate, generating an average winning trade of $180 and average losing trade of $95. The maximum drawdown reached 12% during testing, with available capital of $10,000. The strategy employs fixed stop losses at 25 pips and take profit targets at 45 pips, providing clear risk-reward parameters.
Implementation begins with loading the calculator on the EURUSD 1-hour chart for appropriate timeframe alignment. Parameter configuration includes win rate at 58, average win at 180, and average loss at 95. Total historical trades input receives 89, with account size set to 10000. The stop loss function is enabled with distance set to 25 pips, reflecting the fixed exit strategy. Quarter Kelly mode provides conservative positioning due to the smaller sample size compared to the previous example.
Results demonstrate the impact of smaller sample sizes on Kelly calculations. The risk-reward ratio calculates to 1.89, while the Kelly fraction reaches approximately 32% before adjustments. Sample confidence achieves 89%, providing moderate statistical confidence given the 89 trades. The recommended position settles at approximately 7% after Quarter Kelly application and Bayesian shrinkage adjustment for the smaller sample. Position value amounts to approximately $700, translating to 0.07 standard lots. Risk per trade reaches approximately $175, calculated as 25 pips multiplied by lot size and pip value, with expected value per trade at approximately $49. This conservative position sizing reflects the smaller sample size, with position sizes expected to increase as trade count surpasses 100 and statistical confidence improves.
Example 3: ES1! Futures Systematic Strategy
Systematic futures trading presents unique considerations for Kelly criterion application, as demonstrated by an E-mini S&P 500 futures strategy encompassing 234 total trades. This systematic approach achieved a 45% win rate with an average winning trade of $1,850 and average losing trade of $720. The maximum drawdown reached 18% during the testing period, with available capital of $50,000. The strategy employs 15-tick stop losses with contract specifications of $50 per tick, providing precise risk control mechanisms.
Implementation involves loading the calculator on the ES1! 15-minute chart to align with the systematic trading timeframe. Parameter configuration includes win rate at 45, average win at 1850, and average loss at 720. Total historical trades receives 234, providing robust statistical foundation, with account size set to 50000. The stop loss function is enabled with distance set to 15 ticks, reflecting the systematic exit methodology. Half Kelly mode balances growth potential with appropriate risk management for futures trading.
Results illustrate how favorable risk-reward ratios can support meaningful position sizing despite lower win rates. The risk-reward ratio calculates to 2.57, while the Kelly fraction reaches approximately 16%, lower than previous examples due to the sub-50% win rate. Sample confidence achieves 100% given the 234 trades providing high statistical confidence. The recommended position settles at approximately 8% after Half Kelly adjustment. Estimated margin per contract amounts to approximately $2,500, resulting in a single contract allocation. Position value reaches approximately $2,500, with risk per trade at $750, calculated as 15 ticks multiplied by $50 per tick. Expected value per trade amounts to approximately $508. Despite the lower win rate, the favorable risk-reward ratio supports meaningful position sizing, with single contract allocation reflecting appropriate leverage management for futures trading.
Example 4: MES1! Micro-Futures for Smaller Accounts
Micro-futures contracts provide enhanced accessibility for smaller trading accounts while maintaining identical strategy characteristics. Using the same systematic strategy statistics from the previous example but with available capital of $15,000 and micro-futures specifications of $5 per tick with reduced margin requirements, the implementation demonstrates improved position sizing granularity.
Kelly calculations remain identical to the full-sized contract example, maintaining the same risk-reward dynamics and statistical foundations. However, estimated margin per contract reduces to approximately $250 for micro-contracts, enabling allocation of 4-5 micro-contracts. Position value reaches approximately $1,200, while risk per trade calculates to $75, derived from 15 ticks multiplied by $5 per tick. This granularity advantage provides better position size precision for smaller accounts, enabling more accurate Kelly implementation without requiring large capital commitments.
Example 5: Bitcoin Swing Trading
Cryptocurrency markets present unique challenges requiring modified Kelly application approaches. A Bitcoin swing trading strategy on BTCUSD encompasses 67 total trades with a 71% win rate, generating average winning trades of $3,200 and average losing trades of $1,400. Maximum drawdown reached 28% during testing, with available capital of $30,000. The strategy employs technical analysis for exits without fixed stop losses, relying on price action and momentum indicators.
Implementation requires conservative approaches due to cryptocurrency volatility characteristics. Quarter Kelly mode is recommended despite the high win rate to account for crypto market unpredictability. Expected position sizing remains reduced due to the limited sample size of 67 trades, requiring additional caution until statistical confidence improves. Regular parameter updates are strongly recommended due to cryptocurrency market evolution and changing volatility patterns that can significantly impact strategy performance characteristics.
Advanced Usage Scenarios
Portfolio position sizing requires sophisticated consideration when running multiple strategies simultaneously. Each strategy should have its Kelly fraction calculated independently to maintain mathematical integrity. However, correlation adjustments become necessary when strategies exhibit related performance patterns. Moderately correlated strategies should receive individual position size reductions of 10-20% to account for overlapping risk exposure. Aggregate portfolio risk monitoring ensures total exposure remains within acceptable limits across all active strategies. Professional practitioners often consider using lower fractional Kelly approaches, such as Quarter Kelly, when running multiple strategies simultaneously to provide additional safety margins.
Parameter sensitivity analysis forms a critical component of professional Kelly implementation. Regular validation procedures should include monthly parameter updates using rolling 100-trade windows to capture evolving market conditions while maintaining statistical relevance. Sensitivity testing involves varying win rates by ±5% and average win/loss ratios by ±10% to assess recommendation stability under different parameter assumptions. Out-of-sample validation reserves 20% of historical data for parameter verification, ensuring that optimization doesn't create curve-fitted results. Regime change detection monitors actual performance against expected metrics, triggering parameter reassessment when significant deviations occur.
Risk management integration requires professional overlay considerations beyond pure Kelly calculations. Daily loss limits should cease trading when daily losses exceed twice the calculated risk per trade, preventing emotional decision-making during adverse periods. Maximum position limits should never exceed 25% of account value in any single position regardless of Kelly recommendations, maintaining diversification principles. Correlation monitoring reduces position sizes when holding multiple correlated positions that move together during market stress. Volatility adjustments consider reducing position sizes during periods of elevated VIX above 25 for equity strategies, adapting to changing market conditions.
Troubleshooting and Optimization
Professional implementation often encounters specific challenges requiring systematic troubleshooting approaches. Zero position size displays typically result from insufficient capital for minimum position sizes, negative expected values, or extremely conservative Kelly calculations. Solutions include increasing account size, verifying strategy statistics for accuracy, considering Quarter Kelly mode for conservative approaches, or reassessing overall strategy viability when fundamental issues exist.
Extremely high Kelly fractions exceeding 50% usually indicate underlying problems with parameter estimation. Common causes include unrealistic win rates, inflated risk-reward ratios, or curve-fitted backtest results that don't reflect genuine trading conditions. Solutions require verifying backtest methodology, including all transaction costs in calculations, testing strategies on out-of-sample data, and using conservative fractional Kelly approaches until parameter reliability improves.
Low sample confidence below 50% reflects insufficient historical trades for reliable parameter estimation. This situation demands gathering additional trading data, using Quarter Kelly approaches until reaching 100 or more trades, applying extra conservatism in position sizing, and considering paper trading to build statistical foundations without capital risk.
Inconsistent results across similar strategies often stem from parameter estimation differences, market regime changes, or strategy degradation over time. Professional solutions include standardizing backtest methodology across all strategies, updating parameters regularly to reflect current conditions, and monitoring live performance against expectations to identify deteriorating strategies.
Position sizes that appear inappropriately large or small require careful validation against traditional risk management principles. Professional standards recommend never risking more than 2-3% per trade regardless of Kelly calculations. Calibration should begin with Quarter Kelly approaches, gradually increasing as comfort and confidence develop. Most institutional traders utilize 25-50% of full Kelly recommendations to balance growth with prudent risk management.
Market condition adjustments require dynamic approaches to Kelly implementation. Trending markets may support full Kelly recommendations when directional momentum provides favorable conditions. Ranging or volatile markets typically warrant reducing to Half or Quarter Kelly to account for increased uncertainty. High correlation periods demand reducing individual position sizes when multiple positions move together, concentrating risk exposure. News and event periods often justify temporary position size reductions during high-impact releases that can create unpredictable market movements.
Performance monitoring requires systematic protocols to ensure Kelly implementation remains effective over time. Weekly reviews should compare actual versus expected win rates and average win/loss ratios to identify parameter drift or strategy degradation. Position size efficiency and execution quality monitoring ensures that calculated recommendations translate effectively into actual trading results. Tracking correlation between calculated and realized risk helps identify discrepancies between theoretical and practical risk exposure.
Monthly calibration provides more comprehensive parameter assessment using the most recent 100 trades to maintain statistical relevance while capturing current market conditions. Kelly mode appropriateness requires reassessment based on recent market volatility and performance characteristics, potentially shifting between Full, Half, and Quarter Kelly approaches as conditions change. Transaction cost evaluation ensures that commission structures, spreads, and slippage estimates remain accurate and current.
Quarterly strategic reviews encompass comprehensive strategy performance analysis comparing long-term results against expectations and identifying trends in effectiveness. Market regime assessment evaluates parameter stability across different market conditions, determining whether strategy characteristics remain consistent or require fundamental adjustments. Strategic modifications to position sizing methodology may become necessary as markets evolve or trading approaches mature, ensuring that Kelly implementation continues supporting optimal capital allocation objectives.
Professional Applications
This calculator serves diverse professional applications across the financial industry. Quantitative hedge funds utilize the implementation for systematic position sizing within algorithmic trading frameworks, where mathematical precision and consistent application prove essential for institutional capital management. Professional discretionary traders benefit from optimized position management that removes emotional bias while maintaining flexibility for market-specific adjustments. Portfolio managers employ the calculator for developing risk-adjusted allocation strategies that enhance returns while maintaining prudent risk controls across diverse asset classes and investment strategies.
Individual traders seeking mathematical optimization of capital allocation find the calculator provides institutional-grade methodology previously available only to professional money managers. The Kelly Criterion establishes theoretical foundation for optimal capital allocation across both single strategies and multiple trading systems, offering significant advantages over arbitrary position sizing methods that rely on intuition or fixed percentage approaches. Professional implementation ensures consistent application of mathematically sound principles while adapting to changing market conditions and strategy performance characteristics.
Conclusion
The Kelly Criterion represents one of the few mathematically optimal solutions to fundamental investment problems. When properly understood and carefully implemented, it provides significant competitive advantage in financial markets. This calculator implements modern refinements to Kelly's original formula while maintaining accessibility for practical trading applications.
Success with Kelly requires ongoing learning, systematic application, and continuous refinement based on market feedback and evolving research. Users who master Kelly principles and implement them systematically can expect superior risk-adjusted returns and more consistent capital growth over extended periods.
The extensive academic literature provides rich resources for deeper study, while practical experience builds the intuition necessary for effective implementation. Regular parameter updates, conservative approaches with limited data, and disciplined adherence to calculated recommendations are essential for optimal results.
References
Archer, M. D. (2010). Getting Started in Currency Trading: Winning in Today's Forex Market (3rd ed.). John Wiley & Sons.
Baker, R. D., & McHale, I. G. (2012). An empirical Bayes approach to optimising betting strategies. Journal of the Royal Statistical Society: Series D (The Statistician), 61(1), 75-92.
Breiman, L. (1961). Optimal gambling systems for favorable games. In J. Neyman (Ed.), Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability (pp. 65-78). University of California Press.
Brown, D. B. (1976). Optimal portfolio growth: Logarithmic utility and the Kelly criterion. In W. T. Ziemba & R. G. Vickson (Eds.), Stochastic Optimization Models in Finance (pp. 1-23). Academic Press.
Browne, S., & Whitt, W. (1996). Portfolio choice and the Bayesian Kelly criterion. Advances in Applied Probability, 28(4), 1145-1176.
Estrada, J. (2008). Geometric mean maximization: An overlooked portfolio approach? The Journal of Investing, 17(4), 134-147.
Hakansson, N. H., & Ziemba, W. T. (1995). Capital growth theory. In R. A. Jarrow, V. Maksimovic, & W. T. Ziemba (Eds.), Handbooks in Operations Research and Management Science (Vol. 9, pp. 65-86). Elsevier.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291.
Kaufman, P. J. (2013). Trading Systems and Methods (5th ed.). John Wiley & Sons.
Kelly Jr, J. L. (1956). A new interpretation of information rate. Bell System Technical Journal, 35(4), 917-926.
Lo, A. W., & MacKinlay, A. C. (1999). A Non-Random Walk Down Wall Street. Princeton University Press.
MacLean, L. C., Sanegre, E. O., Zhao, Y., & Ziemba, W. T. (2004). Capital growth with security. Journal of Economic Dynamics and Control, 28(4), 937-954.
MacLean, L. C., Thorp, E. O., & Ziemba, W. T. (2011). The Kelly Capital Growth Investment Criterion: Theory and Practice. World Scientific.
Michaud, R. O. (1989). The Markowitz optimization enigma: Is 'optimized' optimal? Financial Analysts Journal, 45(1), 31-42.
Pabrai, M. (2007). The Dhandho Investor: The Low-Risk Value Method to High Returns. John Wiley & Sons.
Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379-423.
Tharp, V. K. (2007). Trade Your Way to Financial Freedom (2nd ed.). McGraw-Hill.
Thorp, E. O. (2006). The Kelly criterion in blackjack sports betting, and the stock market. In L. C. MacLean, E. O. Thorp, & W. T. Ziemba (Eds.), The Kelly Capital Growth Investment Criterion: Theory and Practice (pp. 789-832). World Scientific.
Van Tharp, K. (2007). Trade Your Way to Financial Freedom (2nd ed.). McGraw-Hill Education.
Vince, R. (1992). The Mathematics of Money Management: Risk Analysis Techniques for Traders. John Wiley & Sons.
Vince, R., & Zhu, H. (2015). Optimal betting under parameter uncertainty. Journal of Statistical Planning and Inference, 161, 19-31.
Ziemba, W. T. (2003). The Stochastic Programming Approach to Asset, Liability, and Wealth Management. The Research Foundation of AIMR.
Further Reading
For comprehensive understanding of Kelly Criterion applications and advanced implementations:
MacLean, L. C., Thorp, E. O., & Ziemba, W. T. (2011). The Kelly Capital Growth Investment Criterion: Theory and Practice. World Scientific.
Vince, R. (1992). The Mathematics of Money Management: Risk Analysis Techniques for Traders. John Wiley & Sons.
Thorp, E. O. (2017). A Man for All Markets: From Las Vegas to Wall Street. Random House.
Cover, T. M., & Thomas, J. A. (2006). Elements of Information Theory (2nd ed.). John Wiley & Sons.
Ziemba, W. T., & Vickson, R. G. (Eds.). (2006). Stochastic Optimization Models in Finance. World Scientific.
SynchroTrend Oscillator (STO) [PhenLabs]📊 SynchroTrend Oscillator
Version: PineScript™ v5
📌 Description
The SynchroTrend Oscillator (STO) is a multi-timeframe synchronization tool that combines trend information from three distinct timeframes into a single, easy-to-interpret oscillator ranging from -100 to +100.
This indicator solves the common problem of having to analyze multiple timeframe charts separately by consolidating trend direction and strength across different time horizons. The STO helps traders identify when markets are truly synchronized across timeframes, potentially indicating stronger trend conditions and higher probability trading opportunities.
Using either Moving Average crossovers or RSI analysis as the trend definition metric, the STO provides a comprehensive view of market structure that adapts to various trading strategies and market conditions.
🚀 Points of Innovation
Triple-timeframe synchronization in a single view eliminates chart switching
Dual trend detection methods (MA vs Price or RSI) for flexibility across different markets
Dynamic color intensity that automatically increases with signal strength
Scaled oscillator format (-100 to +100) for intuitive trend strength interpretation
Customizable signal thresholds to match your risk tolerance and trading style
Visual alerts when markets reach full synchronization states
🔧 Core Components
Trend Scoring System: Calculates a binary score (+1, -1, or 0) for each timeframe based on selected metrics, providing clear trend direction
Multi-Timeframe Synchronization: Combines and scales trend scores from all three timeframes into a single oscillator
Dynamic Visualization: Adjusts color transparency based on signal strength, creating an intuitive visual guide
Threshold System: Provides customizable levels for identifying potentially significant trading opportunities
🔥 Key Features
Triple Timeframe Analysis: Synchronizes three user-defined timeframes (default: 60min, 15min, 5min) into one view
Dual Trend Detection Methods: Choose between Moving Average vs Price or RSI-based trend determination
Adjustable Signal Smoothing: Apply EMA, SMA, or no smoothing to the oscillator output for your preferred signal responsiveness
Dynamic Color Intensity: Colors become more vibrant as signal strength increases, helping identify strongest setups
Customizable Thresholds: Set your own buy/sell threshold levels to match your trading strategy
Comprehensive Alerts: Six different alert conditions for crossing thresholds, zero line, and full synchronization states
🎨 Visualization
Oscillator Line: The main line showing the synchronized trend value from -100 to +100
Dynamic Fill: Area between oscillator and zero line changes transparency based on signal strength
Threshold Lines: Optional dotted lines indicating buy/sell thresholds for visual reference
Color Coding: Green for bullish synchronization, red for bearish synchronization
📖 Usage Guidelines
Timeframe Settings
Timeframe 1: Default: 60 (1 hour) - Primary higher timeframe for trend definition
Timeframe 2: Default: 15 (15 minutes) - Intermediate timeframe for trend definition
Timeframe 3: Default: 5 (5 minutes) - Lower timeframe for trend definition
Trend Calculation Settings
Trend Definition Metric: Default: “MA vs Price” - Method used to determine trend on each timeframe
MA Type: Default: EMA - Moving Average type when using MA vs Price method
MA Length: Default: 21 - Moving Average period when using MA vs Price method
RSI Length: Default: 14 - RSI period when using RSI method
RSI Source: Default: close - Price data source for RSI calculation
Oscillator Settings
Smoothing Type: Default: SMA - Applies smoothing to the final oscillator
Smoothing Length: Default: 5 - Period for the smoothing function
Visual & Threshold Settings
Up/Down Colors: Customize colors for bullish and bearish signals
Transparency Range: Control how transparency changes with signal strength
Line Width: Adjust oscillator line thickness
Buy/Sell Thresholds: Set levels for potential entry/exit signals
✅ Best Use Cases
Trend confirmation across multiple timeframes
Finding high-probability entry points when all timeframes align
Early detection of potential trend reversals
Filtering trade signals from other indicators
Market structure analysis
Identifying potential divergences between timeframes
⚠️ Limitations
Like all indicators, can produce false signals during choppy or ranging markets
Works best in trending market conditions
Should not be used in isolation for trading decisions
Past performance is not indicative of future results
May require different settings for different markets or instruments
💡 What Makes This Unique
Combines three timeframes in a single visualization without requiring multiple chart windows
Dynamic transparency feature that automatically emphasizes stronger signals
Flexible trend definition methods suitable for different market conditions
Visual system that makes multi-timeframe analysis intuitive and accessible
🔬 How It Works
1. Trend Evaluation:
For each timeframe, the indicator calculates a trend score (+1, -1, or 0) using either:
MA vs Price: Comparing close price to a moving average
RSI: Determining if RSI is above or below 50
2. Score Aggregation:
The three trend scores are combined and then scaled to a range of -100 to +100
A value of +100 indicates all timeframes show bullish conditions
A value of -100 indicates all timeframes show bearish conditions
Values in between indicate varying degrees of alignment
3. Signal Processing:
The raw oscillator value can be smoothed using EMA, SMA, or left unsmoothed
The final value determines line color, fill color, and transparency settings
Threshold levels are applied to identify potential trading opportunities
💡 Note:
The SynchroTrend Oscillator is most effective when used as part of a comprehensive trading strategy that includes proper risk management techniques. For best results, consider using the oscillator in conjunction with support/resistance levels, price action analysis, and other complementary indicators that align with your trading style.
