BTC Smart Buy/Sell//@version=5
indicator("BTC Smart Buy/Sell", overlay=true)
// === INPUTS ===
rsiPeriod = input.int(14, "RSI Period")
macdFast = input.int(12, "MACD Fast")
macdSlow = input.int(26, "MACD Slow")
macdSignal = input.int(9, "MACD Signal")
atrPeriod = input.int(10, "ATR Period")
atrMultiplier = input.float(3.0, "Supertrend Multiplier")
volMultiplier = input.float(1.5, "Volume Spike Multiplier")
// === CALCULATIONS ===
rsi = ta.rsi(close, rsiPeriod)
= ta.macd(close, macdFast, macdSlow, macdSignal)
macdCrossUp = ta.crossover(macdLine, signalLine)
macdCrossDown = ta.crossunder(macdLine, signalLine)
// Supertrend
= ta.supertrend(atrMultiplier, atrPeriod)
// Volume spike
avgVol = ta.sma(volume, 20)
volSpike = volume > avgVol * volMultiplier
volDrop = volume < avgVol * 0.7
// === CONDITIONS ===
buyCond = (rsi < 30 ? 1 : 0) + (macdCrossUp ? 1 : 0) + (supertrendDir == 1 ? 1 : 0) + (volSpike ? 1 : 0)
sellCond = (rsi > 70 ? 1 : 0) + (macdCrossDown ? 1 : 0) + (supertrendDir == -1 ? 1 : 0) + (volDrop ? 1 : 0)
buySignal = buyCond >= 3
sellSignal = sellCond >= 3
// === PLOT ===
plotshape(buySignal, title="Buy Signal", location=location.belowbar, color=color.green, style=shape.labelup, text="BUY")
plotshape(sellSignal, title="Sell Signal", location=location.abovebar, color=color.red, style=shape.labeldown, text="SELL")
指標和策略
Position Size Calculatorcalculates what size position you should enter given desired risk and distance from SL in ticks on any symbol
RejectionLibrary "Rejection"
method mergeCandle(h1, l1, c1, h2, l2, o2)
Namespace types: series float, simple float, input float, const float
Parameters:
h1 (float)
l1 (float)
c1 (float)
h2 (float)
l2 (float)
o2 (float)
method isRejectionCandle(candleHigh, candleLow, candleOpen, candleClose)
Namespace types: series float, simple float, input float, const float
Parameters:
candleHigh (float)
candleLow (float)
candleOpen (float)
candleClose (float)
method mergeCandlesForRejection(_numCandles, direction)
Namespace types: series int, simple int, input int, const int
Parameters:
_numCandles (int)
direction (int)
method hasRejection(direction)
Namespace types: series int, simple int, input int, const int
Parameters:
direction (int)
PineTraderOT_V6Library "PineTraderOT_V6"
TODO: Simplify the order ticket generation for Pinetrader.io
GenerateOT(license_id, symbol, action, order_type, trade_type, size, price, tp, sl, risk, trailPrice, trailOffset)
CreateOrderTicket: Establishes a order ticket following appropriate guidelines.
Parameters:
license_id (string) : Provide your license index
symbol (string) : Symbol on which to execute the trade
action (string) : Execution method of the trade : "MRKT" or "PENDING"
order_type (string) : Direction type of the order: "BUY" or "SELL"
trade_type (string) : Is it a "SPREAD" trade or a "SINGLE" symbol execution?
size (float) : Size of the trade, in units
price (float) : If the order is pending you must specify the execution price
tp (float) : (Optional) Take profit of the order
sl (float) : (Optional) Stop loss of the order
risk (float) : Percent to risk for the trade, if size not specified
trailPrice (float) : (Optional) Price at which trailing stop is starting
trailOffset (float) : (Optional) Amount to trail by
Returns: Return Order string
내 스크립트//@version=5
indicator("Support/Resistance Scalping Strategy", overlay=true)
// === 사용자 설정 ===
support_level = input.float(101000, title="지지선", step=10)
resistance_level = input.float(104000, title="저항선", step=10)
rsi = ta.rsi(close, 14)
bb_upper = ta.bb(close, 20, 2).upper
bb_lower = ta.bb(close, 20, 2).lower
// === 조건 ===
// 롱 조건: 지지선 근처 도달 + RSI < 40 + 볼린저 하단 근접
long_condition = (low <= support_level * 1.002) and (rsi < 40) and (close <= bb_lower)
plotshape(long_condition, title="Long Signal", location=location.belowbar, color=color.green, style=shape.labelup, text="LONG")
// 숏 조건: 저항선 근처 도달 + RSI > 60 + 볼린저 상단 근접
short_condition = (high >= resistance_level * 0.998) and (rsi > 60) and (close >= bb_upper)
plotshape(short_condition, title="Short Signal", location=location.abovebar, color=color.red, style=shape.labeldown, text="SHORT")
// 시각적 지지/저항선 표시
hline(support_level, "지지선", color=color.green, linestyle=hline.style_dashed)
hline(resistance_level, "저항선", color=color.red, linestyle=hline.style_dashed)
내 스크립트//@version=5
indicator("Support/Resistance Scalping Strategy", overlay=true)
// === 사용자 설정 ===
support_level = input.float(101000, title="지지선", step=10)
resistance_level = input.float(104000, title="저항선", step=10)
rsi = ta.rsi(close, 14)
bb_upper = ta.bb(close, 20, 2).upper
bb_lower = ta.bb(close, 20, 2).lower
// === 조건 ===
// 롱 조건: 지지선 근처 도달 + RSI < 40 + 볼린저 하단 근접
long_condition = (low <= support_level * 1.002) and (rsi < 40) and (close <= bb_lower)
plotshape(long_condition, title="Long Signal", location=location.belowbar, color=color.green, style=shape.labelup, text="LONG")
// 숏 조건: 저항선 근처 도달 + RSI > 60 + 볼린저 상단 근접
short_condition = (high >= resistance_level * 0.998) and (rsi > 60) and (close >= bb_upper)
plotshape(short_condition, title="Short Signal", location=location.abovebar, color=color.red, style=shape.labeldown, text="SHORT")
// 시각적 지지/저항선 표시
hline(support_level, "지지선", color=color.green, linestyle=hline.style_dashed)
hline(resistance_level, "저항선", color=color.red, linestyle=hline.style_dashed)
Forex Session Levels + Dashboard (AEST)This is a script showing all the key levels you will ever need for the breakout and retest strategy.
Follow my IG:
@liviupircalabu10
Forex Session Levels + Dashboard (AEST)Forex Session Indicators for Breakout and Retest Strategy (AEST)
Position Size CalculatorIt is a position size calculation with 0.05% buffer to take swift entry on either sides with 0.5% risk on your overall capital
Auto Fractal [theUltimator5]I took the awesome indicator provided by theUltimator5 from and implemented the Freeze function by screaming at gemini for the past hour to keep at it until it worked.
Props to theUltimator5 for sharing this script with everyone as opensource.
Trend Persistence Counter (TPC) by riskcipher🧭 Trend Persistence Counter (TPC) – A Simple Price Action Trend Duration Tool
Trend Persistence Counter (TPC) is a lightweight indicator that counts how long a trend persists after a breakout.
It is entirely based on price action, without using any moving averages or smoothing. The goal is to give a simple, rule-based view of trend continuity.
🧠 How It Works (Logic Overview)
This indicator switches between two modes: bullish and bearish.
If close > previous high, the counter enters bullish mode, and starts at +1
While in bullish mode:
If close >= previous low → continue the uptrend → +1 each bar
If close < previous low → trend ends → reset to 0, switch to bearish mode
If close < previous low, the counter enters bearish mode, and starts at -1
While in bearish mode:
If close <= previous high → continue the downtrend → -1 each bar
If close > previous high → trend ends → reset to 0, switch to bullish mode
This provides a bar-by-bar count of trend persistence based on whether price holds structure.
🎯 Use Cases
Track how long a trend continues after a breakout
Quickly detect when trend structure breaks
Help visually filter “strong” vs “weak” moves
Build logic-based alerts (e.g., trend continues for N bars)
🔍 Why Use This Instead of Traditional Indicators?
This is not meant to replace moving averages or trend filters.
But it offers some advantages for those who prefer structure-based logic:
Feature TPC
Based on Price Action ✅ Yes
Uses Lagging Filters ❌ No moving average or smoothing
Trend Duration Measurement ✅ Counts valid consecutive moves
Complexity ⚪ Very simple and transparent
It’s a simple concept and easy to understand, but still useful when combined with other tools or visualized on its own.
⚙️ Technical Notes
Works on any timeframe or instrument
The value is positive during bullish persistence, negative during bearish
Value resets to 0 when trend structure breaks
All logic is calculated bar-by-bar, in real time
✅ Example Usage Ideas
Highlight candles when TPC value crosses a certain threshold (e.g., strong breakout continuation)
Use the zero-cross as a potential reversal warning
Filter trend signals in your existing strategies
1.85I copied this indicator from 4c program so all credit to him/her. I just changed it from 2 SD to 1.85
Line Strategy v6Line Indicator for TradingView
This Pine Script indicator identifies the largest candles on both 5-minute and 1-hour timeframes within the last 240 five-minute bars. It provides visual markers and detailed information to help traders spot significant price movements.
Key Features
Dual Timeframe Analysis:
Identifies largest candle on 5-minute timeframe
Identifies largest candle on 1-hour timeframe (aggregated from 12 five-minute candles)
Visual Markers:
Blue label marks the highest-range 5-minute candle
Purple background highlights the highest-range hourly candle period
Information Table:
Shows price ranges for both timeframes
Displays precise timestamps for identified candles
Color-coded for quick reference
Progress Indicator:
Shows how many bars have been collected (out of 240 required)
How It Works
Data Collection:
Stores high, low, timestamp, and bar index of the last 240 five-minute candles
Automatically updates with each new bar
5-Minute Analysis:
Scans all 5-minute candles to find the one with the largest price range (high - low)
Marks this candle with a blue label showing its range
Hourly Analysis:
Groups 12 five-minute candles to form each hourly candle
Finds the hourly candle with the largest price range
Highlights the entire hour period with a purple background
Information Display:
Creates a table in the top-right corner showing:
Range values for both timeframes
Timestamps of identified candles
Time period of the largest hourly candle
Usage Instructions
Apply the indicator to any 5-minute chart
Wait for the indicator to collect 240 bars (about 20 trading hours)
Results will appear automatically:
Blue label on the largest 5-minute candle
Purple background on the largest hourly candle period
Information table with detailed metrics
Customization Options
You can easily adjust these aspects by modifying the code:
Colors of markers and table cells
Transparency levels of background highlights
Precision of range values displayed
Position of the information table
The indicator is optimized for performance and works in both historical and real-time modes.
Daily 10, 50, 150, 200 DMAIrrespective of the Chart, i.e be it weekly or monthly DMA will be displayed on Daily Values.
Do note that On a weekly chart, this gives you the DMA value from only one daily candle per week, usually Friday’s close. So if a DMA crossover (say, 10-DMA crossing 50-DMA) actually happens on Wednesday, you won’t see that reflected until Friday's value is displayed on the weekly chart. That causes crossover dates to appear wrong or delayed.
Macro Alignment SummaryThis indicator provides a simple, color-coded summary of macro trend alignment across three benchmark assets.
It calculates how many of the selected benchmarks are currently trading above a specified moving average (configurable type, length, and timeframe).
The result is plotted as a score from 0 to 3:
🔴 0: No benchmarks aligned
🟠 1: Weak alignment
🟡 2: Partial alignment
🟢 3: Full macro alignment
🔧 Fully customizable:
Choose any 3 benchmark symbols (e.g., BTC, ETH, QQQ)
Select timeframe (e.g., daily, weekly)
Pick MA type (SMA or EMA) and length (e.g., 21, 50, 200)
Ideal for filtering trades based on broader market strength or for use in a macro trend dashboard alongside entry signals.
Moving Average Convergence DivergenceMACD Update with Histogram off and MACD and signal crossing with a dot signal 1 offset bar ahead of time.
Psychological Levels + Buffer ZonesThis indicator automatically draws major (100-pip) and minor (50-pip) psychological levels on your Forex chart, along with optional buffer zones for smarter trade entries. Zones help you visually capture breakouts, retests, and fakeouts. Includes:
Major & minor psych levels
Adjustable buffer zones (±0.1%, etc.)
Customizable zone color & transparency
Optional ATR trailing lines for trend confirmation
Perfect for scalpers, breakout traders, and zone-based strategies.
HA Reversal StrategyCertainly! Here's a detailed **description (elaboration)** for the **"HA Candle Test"** (i.e., the Heikin Ashi strategy script I just gave you):
---
### 📌 **Script Name**: HA Candle Test
### 📖 **Description**:
This script visualizes **Heikin Ashi candles** and identifies **trend reversal signals** using classic momentum candle behavior — particularly the appearance of **no-wick candles**, which are known to reflect strong directional pressure in Heikin Ashi charts.
It aims to **capture high-probability trend reversals** with minimal noise, relying on the natural smoothing behavior of Heikin Ashi candles.
---
### ✅ **Buy Signal Conditions**:
* At least **two consecutive red Heikin Ashi candles** (indicating a short-term downtrend).
* Followed by a **green Heikin Ashi candle** that has **no lower wick** (i.e., open == low).
* This suggests that **buyers have taken full control**, with no push from sellers — a potential start of an uptrend.
📍 **Interpreted as**: “Market was selling off, but now buyers stepped in strongly — time to consider buying.”
---
### ✅ **Sell Signal Conditions**:
* At least **two consecutive green Heikin Ashi candles** (short-term uptrend).
* Followed by a **red Heikin Ashi candle** that has **no upper wick** (i.e., open == high).
* This implies **sellers are dominating**, with no attempt from buyers to push higher — possible start of a downtrend.
📍 **Interpreted as**: “Market was rallying, but sellers just took over decisively — time to consider selling.”
---
### 📊 **Visual Aids Included**:
* Plots **Heikin Ashi candles** on your main chart for clarity.
* Uses **Buy** and **Sell** label markers (green & red) at signal points.
* Compatible with any timeframe — higher timeframes typically yield stronger signals.
---
### 💡 **Suggested Use**:
* Combine with **support/resistance**, **volume**, or **trend filters** for more robust setups.
* Works well on **1H, 4H, and Daily charts** in trending markets.
* Can be used manually or turned into an automated strategy for backtesting or alerts.
---
Would you like this script packaged as a **strategy()** for backtesting, or would you like me to add **alerts** so you can get notified in real-time when signals appear?
Sticky Notes📌 Sticky Notes - On-Chart Memo Tool
A convenient indicator that lets you display trading ideas and important notes directly on your charts!
✨ Key Features:
📝 Create memos with custom text input
📍 Place anywhere on chart (top/middle/bottom)
🖥️ Screen-fixed display mode (corner positions)
🎨 Fully customizable text and background colors
📏 5 text size options (tiny to huge)
⏰ Time-based display functionality
📐 Text alignment options (left/center/right)
💡 Use Cases:
Trading strategy reminders
Important price level notes
Economic event schedules
Entry/exit point memos
Simple and user-friendly design to enhance your trading analysis!
Wavelet Filter with Adaptive Upsampling [BackQuant]Wavelet Filter with Adaptive Upsampling
The Wavelet Filter with Adaptive Upsampling is an advanced filtering and signal reconstruction tool designed to enhance the analysis of financial time series data. It combines wavelet transforms with adaptive upsampling techniques to filter and reconstruct price data, making it ideal for capturing subtle market movements and enhancing trend detection. This system uses high-pass and low-pass filters to decompose the price series into different frequency components, applying adaptive thresholding to eliminate noise and preserve relevant signal information.
Shout out to Loxx for the Least Squares fitting of trigonometric series and Quinn and Fernandes algorithm for finding frequency
www.tradingview.com
Key Features
1. Frequency Decomposition with High-Pass and Low-Pass Filters:
The indicator decomposes the input time series using high-pass and low-pass filters to separate the high-frequency (detail) and low-frequency (trend) components of the data. This decomposition allows for a more accurate analysis of underlying trends, while mitigating the impact of noise.
2. Soft Thresholding for Noise Reduction:
A soft thresholding function is applied to the high-frequency component, allowing for the reduction of noise while retaining significant market signals. This function adjusts the coefficients of the high-frequency data, removing small fluctuations and leaving only the essential price movements.
3. Adaptive Upsampling Process:
The upsampling process in this script can be customized using different methods: sinusoidal upsampling, advanced upsampling, and simple upsampling. Each method serves a unique purpose:
Sinusoidal Upsample uses a sine wave to interpolate between data points, providing a smooth transition.
Advanced Upsample utilizes a Quinn-Fernandes algorithm to estimate frequency and apply more sophisticated interpolation techniques, adapting to the market’s cyclical behavior.
Simple Upsample linearly interpolates between data points, providing a basic upsampling technique for less complex analysis.
4. Reconstruction of Filtered Signal:
The indicator reconstructs the filtered signal by summing the high and low-frequency components after upsampling. This allows for a detailed yet smooth representation of the original time series, which can be used for analyzing underlying trends in the market.
5. Visualization of Reconstructed Data:
The reconstructed series is plotted, showing how the upsampling and filtering process enhances the clarity of the price movements. Additionally, the script provides the option to visualize the log returns of the reconstructed series as a histogram, with positive returns shown in green and negative returns in red.
6. Cumulative Series and Trend Detection:
A cumulative series is plotted to visualize the compounded effect of the filtered and reconstructed data. This feature helps traders track the overall performance of the asset over time, identifying whether the asset is following a sustained upward or downward trend.
7. Adaptive Thresholding and Noise Estimation:
The system estimates the noise level in the high-frequency component and applies an adaptive thresholding process based on the standard deviation of the downsampled data. This ensures that only significant price movements are retained, further refining the trend analysis.
8. Customizable Parameters for Flexibility:
Users can customize the following parameters to adjust the behavior of the indicator:
Frequency and Phase Shift: Control the periodicity of the wavelet transformation and the phase of the upsampling function.
Upsample Factor: Adjust the level of interpolation applied during the upsampling process.
Smoothing Period: Determine the length of time used to smooth the signal, helping to filter out short-term fluctuations.
References
Enhancing Cross-Sectional Currency Strategies with Context-Aware Learning to Rank
arxiv.org
Daubechies Wavelet - Wikipedia
en.wikipedia.org
Quinn Fernandes Fourier Transform of Filtered Price by Loxx
Note on Usage for Mean-Reversion Strategy
This indicator is primarily designed for trend-following strategies. However, by taking the inverse of the signals, it can be adapted for mean-reversion strategies. This involves buying underperforming assets and selling outperforming ones. Caution: This method may not work effectively with highly correlated assets, as the price movements between correlated assets tend to mirror each other, limiting the effectiveness of mean-reversion strategies.
Final Thoughts
The Wavelet Filter with Adaptive Upsampling is a powerful tool for traders seeking to improve their understanding of market trends and noise. By using advanced wavelet decomposition and adaptive upsampling, this system offers a clearer, more refined picture of price movements, enhancing trend-following strategies. It’s particularly useful for detecting subtle shifts in market momentum and reconstructing price data in a way that removes noise, providing more accurate insights into market conditions.
Volume Momentum [BackQuant]Volume Momentum
The Volume Momentum indicator is designed to help traders identify shifts in market momentum based on volume data. By analyzing the relative volume momentum, this indicator provides insights into whether the market is gaining strength (uptrend) or losing momentum (downtrend). The strategy uses a combination of percentile-based volume normalization, weighted moving averages (WMA), and exponential moving averages (EMA) to assess volume trends.
The system focuses on the relationship between price and volume, utilizing normalized volume data to highlight key market changes. This approach allows traders to focus on volume-driven price movements, helping them to capture momentum shifts early.
Key Features
1. Volume Normalization and Percentile Calculation:
The signed volume (positive when the close is higher than the open, negative when the close is lower) is normalized against the rolling average volume. This normalized volume is then subjected to a percentile interpolation, allowing for a robust statistical measure of how the current volume compares to historical data. The percentile level is customizable, with 50 representing the median.
2. Weighted and Smoothed Moving Averages for Trend Detection:
The normalized volume is smoothed using weighted moving averages (WMA) and exponential moving averages (EMA). These smoothing techniques help eliminate noise, providing a clearer view of the underlying momentum. The WMA filters out short-term fluctuations, while the EMA ensures that the most recent data points have a higher weight, making the system more responsive to current market conditions.
3. Trend Reversal Detection:
The indicator detects momentum shifts by evaluating whether the volume momentum crosses above or below zero. A positive volume momentum indicates a potential uptrend, while a negative momentum suggests a possible downtrend. These trend reversals are identified through crossover and crossunder conditions, triggering alerts when significant changes occur.
4. Dynamic Trend Background and Bar Coloring:
The script offers customizable background coloring based on the trend direction. When volume momentum is positive, the background is colored green, indicating a bullish trend. When volume momentum is negative, the background is colored red, signaling a bearish trend. Additionally, the bars themselves can be colored based on the trend, further helping traders quickly visualize market momentum.
5. Alerts for Momentum Shifts:
The system provides real-time alerts for traders to monitor when volume momentum crosses a critical threshold (zero), signaling a trend reversal. The alerts notify traders when the market momentum turns bullish or bearish, assisting them in making timely decisions.
6. Customizable Parameters for Flexible Usage:
Users can fine-tune the behavior of the indicator by adjusting various parameters:
Volume Rolling Mean: The period used to calculate the average volume for normalization.
Percentile Interpolation Length: Defines the range over which the percentile is calculated.
Percentile Level: Determines the percentile threshold (e.g., 50 for the median).
WMA and Smoothing Periods: Control the smoothing and response time of the indicator.
7. Trend Background Visualization and Trend-Based Bar Coloring:
The background fill is shaded according to whether the volume momentum is positive or negative, providing a visual cue to indicate market strength. Additionally, bars can be color-coded to highlight the trend, making it easier to see the trend’s direction without needing to analyze numerical data manually.
8. Note on Mean-Reversion Strategy:
If you take the inverse of the signals, this indicator can be adapted for a mean-reversion strategy. Instead of following the trend, the strategy would involve buying assets that are underperforming and selling assets that are overperforming, based on volume momentum. However, it’s important to note that this approach may not work effectively on highly correlated assets, as their price movements may be too similar, reducing the effectiveness of the mean-reversion strategy.
Final Thoughts
The Volume Momentum indicator offers a comprehensive approach to analyzing volume-based momentum shifts in the market. By using volume normalization, percentile interpolation, and smoothed moving averages, this system helps identify the strength and direction of market trends. Whether used for trend-following or adapted for mean-reversion, this tool provides traders with actionable insights into the market’s volume-driven movements, improving decision-making and portfolio management.
40 Ticker Cross-Sectional Z-Scores [BackQuant]40 Ticker Cross-Sectional Z-Scores
BackQuant’s 40 Ticker Cross-Sectional Z-Scores is a powerful portfolio management strategy that analyzes the relative performance of up to 40 different assets, comparing them on a cross-sectional basis to identify the top and bottom performers. This indicator computes Z-scores for each asset based on their log returns and evaluates them relative to the mean and standard deviation over a rolling window. The Z-scores represent how far an asset's return deviates from the average, and these values are used to rank the assets, allowing for dynamic asset allocation based on performance.
By focusing on the strongest-performing assets and avoiding the weakest, this strategy aims to enhance returns while managing risk. Additionally, by adjusting for standard deviations, the system offers a risk-adjusted method of ranking assets, making it suitable for traders who want to dynamically allocate capital based on performance metrics rather than just price movements.
Key Features
1. Cross-Sectional Z-Score Calculation:
The system calculates Z-scores for 40 different assets, evaluating their log returns against the mean and standard deviation over a rolling window. This enables users to assess the relative performance of each asset dynamically, highlighting which assets are performing better or worse compared to their historical norms. The Z-score is a useful statistical tool for identifying outliers in asset performance.
2. Asset Ranking and Allocation:
The system ranks assets based on their Z-scores and allocates capital to the top performers. It identifies the top and bottom assets, and traders can allocate capital to the top-performing assets, ensuring that their portfolio is aligned with the best performers. Conversely, the bottom assets are removed from the portfolio, reducing exposure to underperforming assets.
3. Rolling Window for Mean and Standard Deviation Calculations:
The Z-scores are calculated based on rolling means and standard deviations, making the system adaptive to changing market conditions. This rolling calculation window allows the strategy to adjust to recent performance trends and minimize the impact of outdated data.
4. Mean and Standard Deviation Visualization:
The script provides real-time visualizations of the mean (x̄) and standard deviation (σ) of asset returns, helping traders quickly identify trends and volatility in their portfolio. These visual indicators are useful for understanding the current market environment and making more informed allocation decisions.
5. Top & Bottom Performer Tables:
The system generates tables that display the top and bottom performers, ranked by their Z-scores. Traders can quickly see which assets are outperforming and underperforming. These tables provide clear and actionable insights, helping traders make informed decisions about which assets to include in their portfolio.
6. Customizable Parameters:
The strategy allows traders to customize several key parameters, including:
Rolling Calculation Window: Set the window size for the rolling mean and standard deviation calculations.
Top & Bottom Tickers: Choose how many of the top and bottom assets to display and allocate capital to.
Table Orientation: Select between vertical or horizontal table formats to suit the user’s preference.
7. Forward Test & Out-of-Sample Testing:
The system includes out-of-sample forward tests, ensuring that the strategy is evaluated based on real-time performance, not just historical data. This forward testing approach helps validate the robustness of the strategy in dynamic market conditions.
8. Visual Feedback and Alerts:
The system provides visual feedback on the current asset rankings and allocations, with dynamic labels and plots on the chart. Additionally, users receive alerts when allocations change, keeping them informed of important adjustments.
9. Risk Management via Z-Scores and Std Dev:
The system’s approach to asset selection is based on Z-scores, which normalize performance relative to the historical mean. By incorporating standard deviation, it accounts for the volatility and risk associated with each asset. This allows for more precise risk management and portfolio construction.
10. Note on Mean Reversion Strategy:
If you take the inverse of the signals provided by this indicator, the strategy can be used for mean-reversion rather than trend-following. This would involve buying the underperforming assets and selling the outperforming ones. However, it's important to note that this approach does not work well with highly correlated assets, as the relationship between the assets could result in the same directional movement, undermining the effectiveness of the mean-reversion strategy.
References
www.uts.edu.au
onlinelibrary.wiley.com
www.cmegroup.com
Final Thoughts
The 40 Ticker Cross-Sectional Z-Scores strategy offers a data-driven approach to portfolio management, dynamically allocating capital based on the relative performance of assets. By using Z-scores and standard deviations, this strategy ensures that capital is directed to the strongest performers while avoiding weaker assets, ultimately improving the risk-adjusted returns of the portfolio. Whether you’re focused on trend-following or looking to explore mean-reversion strategies, this flexible system can be tailored to suit your investment goals.
