Silent Trigger Silent Trigger combines widely used concepts under one scoring engine. Each module adds weight only when its conditions are met:
1. Higher-Timeframe (HTF) context
• Requests 1H and the next HTF up (e.g., 4H/D) with request.security(...) on confirmed bars only.
• Uses RSI(14) and a MACD line (EMA12–EMA26 difference) for bias.
• By default HTF weights the score. There is an option to require HTF alignment if you prefer a hard filter.
2. Market regime
• ADX for trend strength.
• Bollinger Band width and a fractal-energy proxy to detect squeeze/coiling vs expansion.
3. Smart-money / Wyckoff structure
• High-volume narrow bars, absorption, spring/upthrust, and liquidity grabs past recent swing highs/lows.
4. Momentum & divergences
• RSI and MACD-line divergences (regular + hidden) and simple exhaustion checks.
5. Fair Value Gaps (FVG)
• 3-bar gap with mid-gap revisit confirmation.
6. Volume context
• Relative volume and a compact 10-bin rolling volume profile to locate HVN proximity.
7. Sessions / time filter
• Optional London/NY “kill zone” participation filter.
8. Correlation (optional)
• Simple BTC trend check for USD-quoted markets.
Pre-Move (yellow) logic:
Triggers only when the market is compressed (squeeze/low fractal energy), ADX is rising, the MACD histogram is near zero (pressure building), and there is a money-flow impulse (MFI slope and/or OBV Z-score spike).
The yellow diamond is plotted on the side of the expected move:
• Below for bullish reversals / Above for bullish breakouts.
• Above for bearish reversals / Below for bearish breakouts.
A built-in cooldown keeps yellows from spamming.
⸻
What appears on the chart
• Bull diamond (green): Total score ≥ your threshold and > bear score.
• Bear diamond (magenta): Mirror of the above.
• Pre-move (yellow): Early heads-up; use it with HTF context and structure.
All diamonds are intentionally tiny to minimize clutter.
⸻
Key settings
• Signal Mode & Min Probability – tighten/loosen confirmations.
• Use Higher TF in Scoring – soft weighting (default).
• Require HTF Alignment – optional hard gate.
• Module toggles – Smart Money, Wyckoff, FVG, Correlation, Sessions.
• Pre-Move – enable, cooldown bars, MFI levels, OBV Z-score threshold.
⸻
How to use (practical)
1. Choose a TF that matches your style (5–15m intraday, 1H–4H swing).
2. Read HTF bias first; trade in that direction unless structure clearly supports a reversal.
3. Treat yellow as “get ready.” Act only when a green/magenta prints with structure (S/R, FVG, HVN) and acceptable risk.
4. Place stops beyond the liquidity level or FVG midpoint; size positions conservatively.
⸻
Repainting & HTF policy
• No lookahead is used anywhere.
• request.security is called on confirmed bars; the HTF MACD line is computed inside the HTF context (single series), not by indexing a tuple.
• Signals are designed for bar-close confirmation. Intra-bar alerts can change until the bar closes.
⸻
Limitations (honest)
• Money-flow features depend on volume quality; thin/synthetic volume reduces reliability.
• Pre-moves can fail during unscheduled news shocks or when HTF trend is dominant.
• This is not financial advice. You are responsible for entries, exits, and risk.
⸻
Alerts
Built-in bull/bear alerts include direction and a probability bucket (Basic/Moderate/Strong/Extreme).
Pre-move yellows are primarily visual; you can still set an alert on their plot condition if desired.
⸻
Why this isn’t a “mashup”
• A single probability engine blends HTF bias, structure (liquidity/Wyckoff/FVG), regime, and volume into a score, rather than stacking unrelated indicators.
• A pre-move detector that requires compression + rising trend energy + money-flow impulse, and places the marker on the side of the expected move, with cooldown control.
• A lightweight rolling HVN check to bias continuation vs mean-reversion near key nodes.
⸻
Changelog (summary)
• Current release: pre-move module, HTF hard-gate option, tiny diamonds, clarified HTF/no-repaint policy, session filter tidy-up.
Forecasting
STC Oscillator [Panel]📈 STC Oscillator – Short Description
This indicator plots the STC (Schaff Trend Cycle) line, ranging between 0 and 100, in a dedicated sub-panel.
It is intended to be used together with the main overlay script:
➡ "STC Advanced Signals with Early Warnings "
The oscillator provides the internal basis for all signals, such as:
Early warning pivots
Threshold confirmations
Candlestick alignment
Note:
The main overlay indicator does not show the oscillator line itself, in order to keep the price chart clean. Use this sub-panel version to monitor oscillator trends, divergence, or cycle phases directly.
STC Advanced Signals with Early Warnings [Overlay]🧠 STC Advanced Signals with Early Warnings – Indicator Description
Purpose:
The “STC Advanced Signals” indicator is designed for active traders (day traders, scalpers, swing traders) who require early signal detection without relying solely on one single indicator. It offers a combination of momentum shifts, candlestick confirmation, and visual guidance for high-quality trade setups.
🔍 Core Components
1. Early Warning Arrows (Orange)
Detected using pivot logic based on the internal STC oscillator curve.
Appear 1–3 bars before potential trend shifts.
Warning only – no execution signal yet.
Can alert traders to prepare for setups in advance.
2. Confirmed Signals (Gold Arrows)
Appear after threshold breakouts of the STC oscillator:
Up Arrow: STC crosses above thresholdUp (default: 25).
Down Arrow: STC crosses below thresholdDown (default: 75).
These are execution-level signals and often indicate momentum breakout or reversal confirmation.
3. Tiny Pre-Confirmation Circles (Yellow)
Optional component (can be toggled on/off).
Visualize potential micro-cycles before full signal confirmation.
Useful for anticipating trend continuation or delay.
4. Candlestick Pattern Recognition
Auto-detects 5 reliable patterns, printed one bar after confirmation:
HA = Hammer
SS = Shooting Star
DJ = Doji
EB = Engulfing Bullish
ES = Engulfing Bearish
Labels are plotted above candles and the bar is highlighted yellow.
Ideal for confirming signals via price action structure.
🧰 Chart Setup Recommendations
Best used on M5, M15 (Scalping/Intraday), or H1 (Swing).
Suggested workflow:
Observe orange Early Warning arrow
Wait for confirmed yellow/gold arrow
Confirm with candlestick pattern
Optionally add volume, trend filters (e.g., EMA200)
📊 Technical Notes
This script does not display the STC line itself.
To view the oscillator line (ranging 0–100), add the companion script:
➤ STC Oscillator
This panel-based indicator must be attached in a separate sub-window and mirrors the official cTrader STC calculation.
🛡 Risk Management Suggestions
Always use stop loss: e.g., below hammer low.
Max 0.5% account risk per trade.
Combine multiple signals before executing.
Avoid trading during high-impact news unless backtested.
Stock Scoring SystemThe EMA Scoring System is designed to help traders quickly assess market trend strength and decide portfolio allocation. It compares price vs. key EMAs (21, 50, 100) and also checks the relative strength between EMAs. Based on these conditions, it assigns a score (-6 to +6) and a corresponding allocation percentage.
+6 Score = 100% allocation (strong bullish trend)
-6 Score = 10% allocation (strong bearish trend)
Scores in between represent intermediate trend strength.
📌 Key Features
✅ Scoring Model: Evaluates price vs. EMA alignment and EMA cross relationships.
✅ Allocation % Display: Converts score into suggested portfolio allocation.
✅ Background Highlighting: Green shades for bullish conditions, red shades for bearish.
✅ Customizable Table Position: Choose between Top Right, Top Center, Bottom Right, or Bottom Center.
✅ Toggleable EMAs: Show/Hide 21 EMA, 50 EMA, and 100 EMA directly from indicator settings.
✅ Simple & Intuitive: One glance at the chart tells you trend strength and suggested allocation.
📈 How It Works
Score Calculation:
Price above an EMA = +1, below = -1
Faster EMA above slower EMA = +1, else -1
Maximum score = +6, minimum = -6
Allocation Mapping:
+6 → 100% allocation
+4 to +5 → 100% allocation
+2 to +3 → 75% allocation
0 to +1 → 50% allocation
-1 to -2 → 30% allocation
-3 to -4 → 20% allocation
-5 to -6 → 10% allocation
Visual Output:
Table shows SCORE + Allocation %
Background color shifts with score (green for bullish, red for bearish)
⚠️ Disclaimer
This indicator is for educational purposes only. It does not constitute financial advice. Always backtest and combine with your own analysis before making trading decisions.
NeuroSwarm BTC: Мудрость Толпы vs Эксперты(RUS)
📊 Индикатор проекта NeuroSwarm: «Мудрость Толпы vs Эксперты».
В основу положены ежемесячные опросы по BTC и ETH (1–5 число каждого месяца), проводимые в криптосообществах Telegram.
Толпа — агрегированные прогнозы участников (медиана и среднее).
Эксперты — отдельная группа лидеров мнений, трейдеров и аналитиков.
Все значения фиксируются для месяца и отображаются на графике в виде линий с заливкой диапазонов.
Это позволяет сравнивать ожидания разных групп и соотносить их с реальным движением рынка.
⚠️ Важно: индикатор не является торговым сигналом и используется исключительно для аналитики и визуализации настроений.
Проект NeuroSwarm документирует «мудрость толпы» в крипте и ищет точки совпадения/расхождения с экспертами.
(ENG)
📊 Indicator by NeuroSwarm: “Wisdom of the Crowd vs Experts”.
Based on monthly surveys for BTC & ETH (conducted between the 1st and 5th of each month) within Telegram crypto communities.
Crowd — aggregated forecasts from participants (median & average).
Experts — separate group of opinion leaders, traders, and analysts.
All values are fixed for each month and plotted on the chart as lines with shaded ranges.
This allows to compare expectations of different groups with actual market performance.
⚠️ Note: this indicator is not a trading signal. It’s meant for analytics and sentiment visualization.
The NeuroSwarm project documents the “wisdom of the crowd” in crypto and explores convergence/divergence with experts.
NeuroSwarm ETH: Мудрость Толпы vs Эксперты
(RUS)
📊 Индикатор проекта NeuroSwarm: «Мудрость Толпы vs Эксперты».
В основу положены ежемесячные опросы по BTC и ETH (1–5 число каждого месяца), проводимые в криптосообществах Telegram.
Толпа — агрегированные прогнозы участников (медиана и среднее).
Эксперты — отдельная группа лидеров мнений, трейдеров и аналитиков.
Все значения фиксируются для месяца и отображаются на графике в виде линий с заливкой диапазонов.
Это позволяет сравнивать ожидания разных групп и соотносить их с реальным движением рынка.
⚠️ Важно: индикатор не является торговым сигналом и используется исключительно для аналитики и визуализации настроений.
Проект NeuroSwarm документирует «мудрость толпы» в крипте и ищет точки совпадения/расхождения с экспертами.
(ENG)
📊 Indicator by NeuroSwarm: “Wisdom of the Crowd vs Experts”.
Based on monthly surveys for BTC & ETH (conducted between the 1st and 5th of each month) within Telegram crypto communities.
Crowd — aggregated forecasts from participants (median & average).
Experts — separate group of opinion leaders, traders, and analysts.
All values are fixed for each month and plotted on the chart as lines with shaded ranges.
This allows to compare expectations of different groups with actual market performance.
⚠️ Note: this indicator is not a trading signal. It’s meant for analytics and sentiment visualization.
The NeuroSwarm project documents the “wisdom of the crowd” in crypto and explores convergence/divergence with experts.
Cyclic Reversal Engine [AlgoPoint]Overview
Most indicators focus on price and momentum, but they often ignore a critical third dimension: time. Markets move in rhythmic cycles of expansion and contraction, but these cycles are not fixed; they speed up in trending markets and slow down in choppy conditions.
The Cyclic Reversal Engine is an advanced analytical tool designed to decode this rhythm. Instead of relying on static, lagging formulas, this indicator learns from past market behavior to anticipate when the current trend is statistically likely to reach its exhaustion point, providing high-probability reversal signals.
It achieves this by combining a sophisticated time analysis with a robust price-action confirmation.
How It Works: The Core Logic
The indicator operates on a multi-stage process to identify potential turning points in the market.
1. Market Regime Analysis (The Brain): Before analyzing any cycles, the indicator first diagnoses the current "personality" of the market. Using a combination of the ADX, Choppiness Index, and RSI, it classifies the market into one of three primary regimes:
- Trending: Strong, directional movement.
- Ranging: Sideways, non-directional chop.
- Reversal: An over-extended state (overbought/oversold) where a turn is imminent.
2. Adaptive Cycle Learning (The "Machine Learning" Aspect): This is the indicator's smartest feature. It constantly analyzes past cycles by measuring the bar-count between significant swing highs and swing lows. Crucially, it learns the average cycle duration for each specific market regime. For example, it learns that "in a strong trending market, a new swing low tends to occur every 35 bars," while "in a ranging market, this extends to 60 bars."
3. The Countdown & Timing Signal: The indicator identifies the last major swing high or low and starts a bar-by-bar countdown. Based on the current market regime, it selects the appropriate learned cycle length from its memory. When the bar count approaches this adaptive target, the indicator determines that a reversal is "due" from a timing perspective.
4. Price Confirmation (The Trigger): A signal is never generated based on timing alone. Once the timing condition is met (the cycle is "due"), the indicator waits for a final price-action confirmation. The default confirmation is the RSI entering an extreme overbought or oversold zone, signaling momentum exhaustion. The signal is only triggered when Time + Price Confirmation align.
How to Use This Indicator
- The Dashboard: The panel in the bottom-right corner is your command center.
- Market Regime: Shows the current market personality analyzed by the engine.
- Adaptive Cycle / Bar Count: This is the core of the indicator. It shows the target cycle length for the current regime (e.g., 50) and the current bar count since the last swing point (e.g., 45). The background turns orange when the bar count enters the "due zone," indicating that you should be on high alert for a reversal.
- BUY/SELL Signals: A label appears on the chart only when the two primary conditions are met:
The timing is right (Bar Count has reached the Adaptive Cycle target).
The price confirms exhaustion (RSI is in an extreme zone).
A BUY signal suggests a downtrend cycle is likely complete, and a SELL signal suggests an uptrend cycle is likely complete.
Key Settings
- Pivot Lookback: Controls the sensitivity of the swing point detection. Higher values will identify more significant, longer-term cycles.
- Market Regime Engine: The ADX, Choppiness, and RSI settings can be fine-tuned to adjust how the indicator classifies the market's personality.
- Require Price Confirmation: You can toggle the RSI confirmation on or off. It is highly recommended to keep it enabled for higher-quality signals.
BTC 1D — Trend START/END Signals (clean, no repaint)
This strategy is designed primarily for BTC on the daily (1D) timeframe in TradingView.
BUY (start of uptrend)
Fast EMA is above Slow EMA.
Price breaks above the previous Donchian high.
Optional filters (if enabled): volume surge and strong momentum/RSI.
Only one BUY per uptrend—no additional buys until a SELL occurs.
SELL (end of uptrend)
Price falls below the previous Donchian low, or
Price drops below the Slow EMA, or
Momentum flips bearish (DI− > DI+ or RSI ≤ threshold).
One SELL marks the end of the uptrend.
RockstarrFX — Stochastic OB/OS Cross SignalsThe RockstarrFX Stochastic Cross Strategy (5/3/3) is a clean, professional-grade tool that plots %K and %D lines and generates buy/sell signals only in high-probability zones.
🔑 How it works:
Buy (B): %K crosses above %D in/near oversold (≤22)
Sell (S): %K crosses below %D in/near overbought (≥78)
⚙️ Features:
Built on the classic Stochastic 5/3/3 oscillator
Signals filtered to appear only in OB/OS regions (reducing false triggers)
Default label size = Tiny (with options for Small/Normal)
Optional OB/OS shading for quick context
Mono-inspired muted colors for a clean charting experience
🔥 Designed for traders who rely on momentum shifts, reversals, and confluence setups. Works across all timeframes — forex, crypto, indices, and stocks.
🔍 Keywords (SEO): stochastic oscillator, stochastic cross strategy, overbought oversold signals, stochastic indicator, momentum trading, stochastic trading system, buy sell signals.
⚡ Part of the RockstarrFX 3-Step Setup Toolkit.
⚠️ Disclaimer: This script is published for educational purposes only. It is not financial advice and does not constitute a recommendation to buy or sell any financial instrument. Past performance is not indicative of future results. Always test on demo before using in live markets and trade responsibly.
Most-Crossed Channels (FAST • Top-K • Flexible Window)//@version=5
indicator("Most-Crossed Channels (FAST • Top-K • Flexible Window)", overlay=true, max_boxes_count=60, max_labels_count=60)
// ---------- Inputs ----------
windowMode = input.string(defval="Last N Bars", title="Scan Window", options= )
barsLookback = input.int(defval=800, title="If Last N Bars → how many?", minval=100, maxval=5000)
sess = input.session(defval="0830-1500", title="Session (exchange tz)")
sessionsBack = input.int(defval=1, title="If Last N Sessions → how many?", minval=1, maxval=10)
minutesLookback = input.int(defval=120, title="If Last X Minutes → how many?", minval=5, maxval=24*60)
sinceTs = input.time(defval=timestamp("2024-01-01T09:30:00"), title="Since time (chart tz)")
channelsK = input.int(defval=3, title="How many channels (Top-K)?", minval=1, maxval=10)
binTicks = input.int(defval=8, title="Bin width (ticks)", minval=1, maxval=200) // NQ tick=0.25; 8 ticks = 2.0 pts
minSepTicks = input.int(defval=12, title="Min separation between channels (ticks)", minval=1, maxval=500)
countSource = input.string(defval="Wick (H-L)", title="Count bars using", options= )
drawMode = input.string(defval="Use Candle", title="Draw channel as", options= )
anchorPart = input.string(defval="Body", title="If Use Candle → part", options= )
fixedTicks = input.int(defval=8, title="If Fixed Thickness → thickness (ticks)", minval=1, maxval=200)
extendBars = input.int(defval=400, title="Extend to right (bars)", minval=50, maxval=5000)
showLabels = input.bool(defval=true, title="Show labels with counts")
// ---------- Colors ----------
colFill = color.new(color.blue, 78)
colEdge = color.new(color.blue, 0)
colTxt = color.white
// ---------- Draw caches (never empty) ----------
var box g_boxes = array.new_box()
var label g_lbls = array.new_label()
// ---------- Helpers ----------
barsFromMinutes(mins, avgBarMs) =>
ms = mins * 60000.0
int(math.max(2, math.round(ms / nz(avgBarMs, 60000.0))))
// First (oldest) candle in whose selected part contains `level`
anchorIndexForPrice(level, useBody, scanNLocal) =>
idx = -1
for m = 1 to scanNLocal - 1
k = scanNLocal - m // oldest → newest
o = open
c = close
h = high
l = low
topZ = useBody ? math.max(o, c) : h
botZ = useBody ? math.min(o, c) : l
if level >= botZ and level <= topZ
idx := k
break
idx
// ---------- Window depth ----------
inSess = not na(time(timeframe.period, sess))
sessStartIdx = ta.valuewhen(inSess and not inSess , bar_index, 0)
sessStartIdxN = ta.valuewhen(inSess and not inSess , bar_index, sessionsBack - 1)
sinceStartIdx = ta.valuewhen(time >= sinceTs and time < sinceTs, bar_index, 0)
avgBarMs = ta.sma(time - time , 50)
depthRaw = switch windowMode
"Last N Bars" => barsLookback
"Today (session)" => bar_index - nz(sessStartIdx, bar_index)
"Last N Sessions" => bar_index - nz(sessStartIdxN, bar_index)
"Last X Minutes" => barsFromMinutes(minutesLookback, avgBarMs)
"Since time" => bar_index - nz(sinceStartIdx, bar_index)
avail = bar_index + 1
scanN = math.min(avail, math.max(2, depthRaw))
scanN := math.min(scanN, 2000) // performance cap
// ---------- Early guard ----------
if scanN < 2
na
else
// ---------- Build price histogram (O(N + B)) ----------
priceMin = 10e10
priceMax = -10e10
for j = 0 to scanN - 1
loB = math.min(open , close )
hiB = math.max(open , close )
lo = (countSource == "Body only") ? loB : low
hi = (countSource == "Body only") ? hiB : high
priceMin := math.min(priceMin, nz(lo, priceMin))
priceMax := math.max(priceMax, nz(hi, priceMax))
rng = priceMax - priceMin
tick = syminfo.mintick
binSize = tick * binTicks
if na(rng) or rng <= 0 or binSize <= 0
na
else
// Pre-allocate fixed-size arrays (never size 0)
MAX_BINS = 600
var float diff = array.new_float(MAX_BINS + 2, 0.0) // +2 so iH+1 is safe
var float counts = array.new_float(MAX_BINS + 1, 0.0)
var int blocked = array.new_int(MAX_BINS + 1, 0)
var int topIdx = array.new_int()
binsN = math.max(1, math.min(MAX_BINS, int(math.ceil(rng / binSize)) + 1))
// reset slices
for i = 0 to binsN + 1
array.set(diff, i, 0.0)
for i = 0 to binsN
array.set(counts, i, 0.0)
array.set(blocked, i, 0)
array.clear(topIdx)
// Range adds
for j = 0 to scanN - 1
loB = math.min(open , close )
hiB = math.max(open , close )
lo = (countSource == "Body only") ? loB : low
hi = (countSource == "Body only") ? hiB : high
iL = int(math.floor((lo - priceMin) / binSize))
iH = int(math.floor((hi - priceMin) / binSize))
iL := math.max(0, math.min(binsN - 1, iL))
iH := math.max(0, math.min(binsN - 1, iH))
array.set(diff, iL, array.get(diff, iL) + 1.0)
array.set(diff, iH + 1, array.get(diff, iH + 1) - 1.0)
// Prefix sum → counts
run = 0.0
for b = 0 to binsN - 1
run += array.get(diff, b)
array.set(counts, b, run)
// Top-K with spacing
sepBins = math.max(1, int(math.ceil(minSepTicks / binTicks)))
picks = math.min(channelsK, binsN)
if picks > 0
for _ = 0 to picks - 1
bestVal = -1e9
bestBin = -1
for b = 0 to binsN - 1
if array.get(blocked, b) == 0
v = array.get(counts, b)
if v > bestVal
bestVal := v
bestBin := b
if bestBin >= 0
array.push(topIdx, bestBin)
lB = math.max(0, bestBin - sepBins)
rB = math.min(binsN - 1, bestBin + sepBins)
for bb = lB to rB
array.set(blocked, bb, 1)
// Clear old drawings safely
while array.size(g_boxes) > 0
box.delete(array.pop(g_boxes))
while array.size(g_lbls) > 0
label.delete(array.pop(g_lbls))
// Draw Top-K channels
sz = array.size(topIdx)
if sz > 0
for t = 0 to sz - 1
b = array.get(topIdx, t)
level = priceMin + (b + 0.5) * binSize
useBody = (drawMode == "Use Candle")
anc = anchorIndexForPrice(level, useBody, scanN)
anc := anc == -1 ? scanN - 1 : anc
oA = open
cA = close
hA = high
lA = low
float topV = na
float botV = na
if drawMode == "Use Candle"
topV := (anchorPart == "Body") ? math.max(oA, cA) : hA
botV := (anchorPart == "Body") ? math.min(oA, cA) : lA
else
half = (fixedTicks * tick) * 0.5
topV := level + half
botV := level - half
left = bar_index - anc
right = bar_index + extendBars
bx = box.new(left, topV, right, botV, xloc=xloc.bar_index, bgcolor=colFill, border_color=colEdge, border_width=2)
array.push(g_boxes, bx)
if showLabels
txt = str.tostring(int(array.get(counts, b))) + " crosses"
lb = label.new(left, topV, txt, xloc=xloc.bar_index, style=label.style_label_down, textcolor=colTxt, color=colEdge)
array.push(g_lbls, lb)
Sinyal Gabungan Lengkap (TWAP + Vol + Waktu)Sinyal Gabungan Lengkap (TWAP + Vol + Waktu) volume btc dan total3 dan ema
Bull/Bear Flag + 9-21 EMA Cross with Targetssimple chart indicator help with buy sell targets using bear and bull flag along with moving averages on chart -helpful for beginner traders
🟥 Synthetic 10Y Real Yield (US10Y - Breakeven)This script calculates and plots a synthetic U.S. 10-Year Real Yield by subtracting the 10-Year Breakeven Inflation Rate (USGGBE10) from the nominal 10-Year Treasury Yield (US10Y).
Real yields are a core macro driver for gold, crypto, growth stocks, and bond pricing, and are closely monitored by institutional traders.
The script includes key reference lines:
0% = Below zero = deeply accommodative regime
1.5% = Common threshold used by macro desks to evaluate gold upside breakout conditions
📈 Use this to monitor macro shifts in real-time and front-run capital flows during major CPI, NFP, and Fed events.
Update Frequency: Daily (based on Treasury market data)
Spiderlines BTCUSD - daily/weekly📘 Documentation – Daily and Weekly Spider Lines for Bitcoin
🔹 Purpose of the Script
This script draws dynamic “Spider Lines” in the Bitcoin chart.
The lines connect certain historical candles with a reference candle and extend to the right.
These act as guideline levels that can serve as potential support or resistance zones.
🔹 How It Works
The script operates in two modes, depending on the active chart timeframe:
Weekly Mode (timeframe.isweekly)
The reference date is July 1, 2019.
The number of weeks since that date is calculated.
This defines the connection candle (connection_candle).
Several predefined offsets (e.g., +32, +34, +36 …) are added to the reference to determine starting candles.
Lines are drawn from these candles toward the connection candle.
→ Line color: green
Daily Mode (timeframe.isdaily)
Same reference date: July 1, 2019.
The number of days since that date is calculated.
Again, a connection candle is set.
A different set of offsets (e.g., +224, +238, +252 …) defines the starting candles.
Lines are drawn accordingly.
→ Line color: red
🔹 Line Logic
Each line connects:
Start → bar_index at high
End → bar_index at close
Lines are extended indefinitely to the right (extend.right).
Appearance: dashed style, width 2.
🔹 Error Handling
If a calculated candle index does not exist in the chart history (e.g., chart data does not go back far enough),
a label is plotted in the chart showing the message:
"Daily idx out of range: 252"
This way, missing lines can be diagnosed easily.
🔹 Color Convention
Weekly Spider Lines → Green
Daily Spider Lines → Red
🔹 Use Cases
Visualization of historical cyclic line patterns.
Helps in technical chart analysis: spotting potential reaction zones in price movement.
Designed mainly for long-term traders and analysts observing Bitcoin in Daily or Weekly timeframes.
🔹 Limitations
Works only on Daily and Weekly charts.
Requires chart data going back to July 1, 2019.
Based purely on fixed offsets → not a classical indicator like Moving Averages or RSI.
Analyst Targets ProbabilityThis indicator calculates the probability of the current stock price reaching or exceeding the analyst-provided high, average, and low price targets within a one-year time horizon. It utilizes a geometric Brownian motion (GBM) model, a standard approach in financial modeling that assumes log-normal price distribution with constant volatility.
### Key Features:
- **Analyst Targets**: Automatically pulls the high, average, and low one-year price targets from TradingView's syminfo data.
- **Risk-Free Rate**: Fetched from the 1-year US Treasury yield (symbol: TVC:US01Y). Defaults to 4% if unavailable.
- **Dividend Yield**: Uses trailing twelve-month (TTM) dividends per share (DPS) from financial data, divided by current price. Defaults to 0% if unavailable.
- **Volatility**: Computed as annualized historical volatility based on 252 trading days of daily log returns. Falls back to a 20-day period if insufficient data, or defaults to 30% if still unavailable.
- **Probability Calculation**: Employs the barrier hitting probability formula under GBM:
- Drift (μ) = risk-free rate - dividend yield - (volatility² / 2)
- The formula for probability P of hitting target H from current price S₀ over time T is:
P = Φ(d₊) + (H / S₀)^p ⋅ Φ(d₋) for H > S₀ (or adjusted for H < S₀)
Where l = ln(max(H, S₀)/min(H, S₀)), ν = drift, p = -2ν / σ², d₊ = (-l + νT) / (σ√T), d₋ = (-l - νT) / (σ√T), and Φ is the standard normal CDF (approximated using a polynomial method for accuracy).
- **Output Display**: A table in the top-right corner shows each target type, its value, and the estimated probability (as a percentage). "N/A" appears if data is unavailable or calculations cannot proceed (e.g., zero volatility).
### Assumptions and Limitations:
- Assumes constant volatility and drift, no transaction costs, and continuous trading (real markets may deviate due to jumps, news events, or changing conditions).
- Probabilities are model-based estimates and not guarantees; they represent the likelihood under risk-neutral measure.
- Best suited for stocks with available analyst targets and historical data; may default to assumptions for less-liquid symbols.
- No user inputs required—fully automated using TradingView's data sources.
This script is provided under the Mozilla Public License 2.0. For educational and informational purposes only; not financial advice. Test on your charts and consider backtesting for validation.
Monthly MA Box for S&P 500 or othersThis moving average helps detect when the asset is undervalued or overvalued. Users can adjust the spread between the moving averages.
Major Wars with a signifiant economic impactThis indicator highlights major wars that have had a significant economic impact worldwide. It allows users to easily see their effects on the charts.
Dow Theory Indicator## 🎯 Key Features of the Indicator
### 📈 Complete Implementation of Dow Theory
- Three-tier trend structure: primary trend (50 periods), secondary trend (20 periods), and minor trend (10 periods).
- Swing point analysis: automatically detects critical swing highs and lows.
- Trend confirmation mechanism: strict confirmation logic based on consecutive higher highs/higher lows or lower highs/lower lows.
- Volume confirmation: ensures price moves are supported by trading volume.
### 🕐 Flexible Timeframe Parameters
All key parameters are adjustable, making it especially suitable for U.S. equities:
Trend analysis parameters:
- Primary trend period: 20–200 (default 50; recommended 50–100 for U.S. stocks).
- Secondary trend period: 10–100 (default 20; recommended 15–30 for U.S. stocks).
- Minor trend period: 5–50 (default 10; recommended 5–15 for U.S. stocks).
Dow Theory parameters:
- Swing high/low lookback: 5–50 (default 10).
- Trend confirmation bar count: 1–10 (default 3).
- Volume confirmation period: 10–100 (default 20).
### 🇺🇸 U.S. Market Optimizations
- Session awareness: distinguishes Regular Trading Hours (9:30–16:00 EST) from pre-market and after-hours.
- Pre/post-market weighting: adjustable weighting factor for signals during extended hours.
- Earnings season filter: automatically adjusts sensitivity during earnings periods.
- U.S.-optimized default parameters.
## 🎨 Visualization
1. Trend lines: three differently colored trend lines.
2. Background fill: green (uptrend) / red (downtrend) / gray (neutral).
3. Signal markers: arrows, labels, and warning icons.
4. Swing point markers: small triangles at key turning points.
5. Info panel: real-time display of eight key metrics.
## 🚨 Alert System
- Trend turning to up/down.
- Strong bullish/bearish signals (dual confirmation).
- Volume divergence warning.
- New swing high/low formed.
## 📋 How to Use
1. Open the Pine Editor in TradingView.
2. Copy the contents of dow_theory_indicator.pine.
3. Paste and click “Add to chart.”
4. Adjust parameters based on trading style:
- Long-term investing: increase all period parameters.
- Swing trading: use the default parameters.
- Short-term trading: decrease all period parameters.
## 💡 Parameter Tips for U.S. Stocks
- Large-cap blue chips (AAPL, MSFT): primary 60–80, secondary 25–30.
- Mid-cap growth stocks: primary 40–60, secondary 18–25.
- Small-cap high-volatility stocks: primary 30–50, secondary 15–20.
SMC Analysis - Fair Value Gaps (Enhanced)SMC Analysis - Fair Value Gaps (Enhanced) Script Summary
Overview
The "SMC Analysis - Fair Value Gaps (Enhanced)" script, written in Pine Script (version 6), is a technical analysis indicator designed for TradingView to identify and visualize Fair Value Gaps (FVGs) on a price chart. It supports both the main timeframe and multiple higher timeframes (MTF) for comprehensive market analysis. FVGs are price gaps formed by a three-candle pattern, indicating potential areas of market inefficiency where price may return to fill the gap.
Key Features
FVG Detection:
Identifies bullish FVGs: Occur when the high of a candle two bars prior is lower than the low of the current candle, with the middle candle being bullish (close > open).
Identifies bearish FVGs: Occur when the low of a candle two bars prior is higher than the high of the current candle, with the middle candle being bearish (close < open).
Visualizes FVGs as colored boxes on the chart (green for bullish, red for bearish).
Mitigation Tracking:
Tracks when FVGs are touched (price overlaps the gap range) or mitigated (price fully closes the gap).
Strict Mode: Marks an FVG as mitigated when price touches the gap range.
Normal Mode: Requires a full breakthrough (price crossing the gap’s bottom for bullish FVGs or top for bearish FVGs) for mitigation.
Optionally converts FVG box borders to dashed lines and increases transparency when partially touched.
Multi-Timeframe (MTF) Support:
Analyzes FVGs on three user-defined higher timeframes (default: 15m, 60m, 240m).
Displays MTF FVGs with distinct labels and slightly more transparent colors.
Ensures no duplicate processing of MTF bars to maintain performance.
Customization Options:
FVG Length: Adjustable duration for how long FVGs are displayed (default: 20 bars).
Show/Hide FVGs: Toggle visibility for main timeframe and each MTF.
Color Customization: User-defined colors for bullish and bearish FVGs (default: green and red).
Display Options: Toggle for showing dashed lines after partial touches and strict mitigation mode.
Performance Optimization:
Limits the number of displayed boxes (50 for main timeframe, 20 per MTF) to prevent performance issues.
Automatically removes older boxes to maintain a clean chart.
Functionality
Visualization: Draws boxes around detected FVGs, with customizable colors and text labels ("FVG" for main timeframe, "FVG " for MTF).
Dynamic Updates: Extends or terminates FVG boxes based on mitigation status and user settings.
Efficient Storage: Uses arrays to manage FVG data (boxes, tops, bottoms, indices, mitigation status, and touch status) separately for main and MTF analyses.
Use Case
This indicator is designed for traders using Smart Money Concepts (SMC) to identify areas of market inefficiency (FVGs) for potential price reversals or continuations. The MTF support allows analysis across different timeframes, aiding in confirming trends or spotting higher-timeframe support/resistance zones.
Seasonality - Multiple Timeframes📊 Seasonality - Multiple Timeframes
🎯 What This Indicator Does
This advanced seasonality indicator analyzes historical price patterns across multiple configurable timeframes and projects future seasonal behavior based on statistical averages. Unlike simple seasonal overlays, this indicator provides gap-resistant architecture specifically designed for commodity futures markets and other instruments with contract rolls.
🔧 Key Features
Multiple Timeframe Analysis
Three Independent Timeframes: Configure separate historical periods (e.g., 5Y, 10Y, 15Y) for comprehensive analysis
Individual Control: Enable/disable historical lines and projections independently for each timeframe
Color Customization: Distinct colors for historical patterns and future projections
Advanced Architecture
Gap-Resistant Design: Handles missing data and contract rolls in futures markets seamlessly
Calendar-Day Normalization: Uses 365-day calendar system for accurate seasonal comparisons
Outlier Filtering: Automatically excludes extreme price movements (>10% daily changes)
Roll Detection: Identifies and excludes contract roll periods to maintain data integrity
Real-Time Projections
Forward-Looking Analysis: Projects seasonal patterns into the future based on remaining calendar days
Configurable Projection Length: Adjust forecast period from 10 to 150 bars
Data Interpolation: Optional gap-filling for smoother seasonal curves
📈 How It Works
Data Collection Process
The indicator collects daily price returns for each calendar day (1-365) over your specified historical periods. For each timeframe, it:
Calculates daily returns while excluding roll periods and outliers
Accumulates these returns by calendar day across multiple years
Computes average seasonal performance from January 1st to current date
Projects remaining seasonal pattern based on historical averages
🎯 Designed For
Primary Use Cases
Commodity Futures Trading: Corn, soybeans, coffee, sugar, cocoa, natural gas, crude oil
Seasonal Strategy Development: Identify optimal entry/exit timing based on historical patterns
Pattern Validation: Confirm seasonal tendencies across different time horizons
Market Timing: Compare current performance against historical seasonal expectations
Trading Applications
Trend Confirmation: Use multiple timeframes to validate seasonal direction
Risk Assessment: Understand seasonal volatility patterns
Position Sizing: Adjust exposure based on seasonal performance consistency
Calendar Spread Analysis: Identify seasonal price relationships
⚙️ Configuration Guide
Timeframe Setup
Configure each timeframe independently:
Years: Set historical lookback period (1-20 years)
Historical Display: Show/hide the seasonal pattern line
Projection Display: Enable/disable future seasonal projection
Colors: Customize line colors for visual clarity
Display Options
Current YTD: Compare actual year-to-date performance
Info Table: Detailed performance comparison across timeframes
Projection Bars: Control forward-looking projection length
Fill Gaps: Interpolate missing data points for smoother curves
Debug Features
Enable debug mode to validate data quality:
Data Point Counts: Verify sufficient historical data per calendar day
Roll Detection Status: Monitor contract roll identification
Empty Days Analysis: Identify potential data gaps
Calculation Verification: Debug seasonal price computations
📊 Interpretation Guidelines
Strong Seasonal Signal
All three timeframes align in the same direction
Current price follows seasonal expectation
Sufficient data points (>3 years minimum per timeframe)
Seasonal Divergence
Different timeframes show conflicting patterns
Recent years deviate from longer-term averages
Current price significantly above/below seasonal expectation
Data Quality Indicators
Green Status: Adequate data across all calendar days
Red Warnings: Insufficient data or excessive gaps
Roll Detection: Proper handling of futures contract changes
⚠️ Important Considerations
Data Requirements
Minimum History: At least 3-5 years for reliable seasonal analysis
Continuous Data: Best results with daily continuous contract data
Market Hours: Designed for traditional market session data
Limitations
Past Performance: Historical patterns don't guarantee future results
Market Changes: Structural shifts can alter traditional seasonal patterns
External Factors: Weather, geopolitics, and policy changes affect seasonal behavior
Contract Rolls: Some data gaps may occur during futures roll periods
🔍 Technical Specifications
Performance Optimizations
Array Management: Efficient data storage using Pine Script arrays
Gap Handling: Robust price calculation with fallback mechanisms
Memory Usage: Optimized for large historical datasets (max_bars_back = 4000)
Real-Time Updates: Live calculation updates as new data arrives
Calculation Accuracy
Outlier Filtering: Excludes daily moves >10% to prevent data distortion
Roll Detection: 8% threshold for identifying contract changes
Data Validation: Multiple checks for price continuity and data integrity
🚀 Getting Started
Add to Chart: Apply indicator to your desired futures contract or commodity
Configure Timeframes: Set historical periods (recommend 5Y, 10Y, 15Y)
Enable Projections: Turn on future seasonal projections for forward guidance
Validate Data: Use debug mode initially to ensure sufficient historical data
Interpret Patterns: Compare current price action against seasonal expectations
💡 Pro Tips
Multiple Confirmations: Use all three timeframes for stronger signal validation
Combine with Technicals: Integrate seasonal analysis with technical indicators
Monitor Divergences: Pay attention when current price deviates from seasonal pattern
Adjust for Volatility: Consider seasonal volatility patterns for position sizing
Regular Updates: Recalibrate settings annually to maintain relevance
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This indicator represents years of development focused on commodity market seasonality. It provides institutional-grade seasonal analysis previously available only to professional trading firms.
Machine Learning : Neural Network Prediction -EasyNeuro-Machine Learning: Neural Network Prediction
— An indicator that learns and predicts price movements using a neural network —
Overview
The indicator “Machine Learning: Neural Network Prediction” uses price data from the chart and applies a three-layer Feedforward Neural Network (FNN) to estimate future price movements.
Key Features
Normally, training and inference with neural networks require advanced programming languages that support machine learning frameworks (such as TensorFlow or PyTorch) as well as high-performance hardware with GPUs. However, this indicator independently implements the neural network mechanism within TradingView’s Pine Script environment, enabling real-time training and prediction directly on the chart.
Since Pine Script does not support matrix operations, the backpropagation algorithm—necessary for neural network training—has been implemented entirely through scalar operations. This unique approach makes the creation of such a groundbreaking indicator possible.
Significance of Neural Networks
Neural networks are a core machine learning method, forming the foundation of today’s widely used generative AI systems, such as OpenAI’s GPT and Google’s Gemini. The feedforward neural network adopted in this indicator is the most classical architecture among neural networks. One key advantage of neural networks is their ability to perform nonlinear predictions.
All conventional indicators—such as moving averages and oscillators like RSI—are essentially linear predictors. Linear prediction inherently lags behind past price fluctuations. In contrast, nonlinear prediction makes it theoretically possible to dynamically anticipate future price movements based on past patterns. This offers a significant benefit for using neural networks as prediction tools among the multitude of available indicators.
Moreover, neural networks excel at pattern recognition. Since technical analysis is largely based on recognizing market patterns, this makes neural networks a highly compatible approach.
Structure of the Indicator
This indicator is based on a three-layer feedforward neural network (FNN). Every time a new candlestick forms, the model samples random past data and performs online learning using stochastic gradient descent (SGD).
SGD is known as a more versatile learning method compared to standard gradient descent, particularly effective for uncertain datasets like financial market price data. Considering Pine Script’s computational constraints, SGD is a practical choice since it can learn effectively from small amounts of data. Because online learning is performed with each new candlestick, the indicator becomes a little “smarter” over time.
Adjustable Parameters
Learning Rate
Specifies how much the network’s parameters are updated per training step. Values between 0.0001 and 0.001 are recommended. Too high causes divergence and unstable predictions, while too low prevents sufficient learning.
Iterations per Online Learning Step
Specifies how many training iterations occur with each new candlestick. More iterations improve accuracy but may cause timeouts if excessive.
Seed
Random seed for initializing parameters. Changing the seed may alter performance.
Architecture Settings
Number of nodes in input and hidden layers:
Increasing input layer nodes allows predictions based on longer historical periods. Increasing hidden layer nodes increases the network’s interpretive capacity, enabling more flexible nonlinear predictions. However, more nodes increase computational cost exponentially, risking timeouts and overfitting.
Hidden layer activation function (ReLU / Sigmoid / Tanh):
Sigmoid:
Classical function, outputs between 0–1, approximates a normal distribution.
Tanh:
Similar to Sigmoid but outputs between -1 and 1, centered around 0, often more accurate.
ReLU:
Simple function (outputs input if ≥ 0, else 0), efficient and widely effective.
Input Features (selectable and combinable)
RoC (Rate of Change):
Measures relative price change over a period. Useful for predicting movement direction.
RSI (Relative Strength Index):
Oscillator showing how much price has risen/fallen within a period. Widely used to anticipate direction and momentum.
Stdev (Standard Deviation, volatility):
Measures price variability. Useful for volatility prediction, though not directional.
Optionally, input data can be smoothed to stabilize predictions.
Other Parameters
Data Sampling Window:
Period from which random samples are drawn for SGD.
Prediction Smoothing Period:
Smooths predictions to reduce spikes, especially when RoC is used.
Prediction MA Period:
Moving average applied to smoothed predictions.
Visualization Features
The internal state of the neural network is displayed in a table at the upper-right of the chart:
Network architecture:
Displays the structure of input, hidden, and output layers.
Node activations:
Shows how input, hidden, and output node values dynamically change with market conditions.
This design allows traders to intuitively understand the inner workings of the neural network, which is often treated as a black box.
Glossary of Terms
Feature:
Input variables fed to the model (RoC/RSI/Stdev).
Node/Unit:
Smallest computational element in a layer.
Activation Function:
Nonlinear function applied to node outputs (ReLU/Sigmoid/Tanh).
MSE (Mean Squared Error):
Loss function using average squared errors.
Gradient Descent (GD/SGD):
Optimization method that gradually adjusts weights in the direction that reduces loss.
Online Learning:
Training method where the model updates sequentially with each new data point.
