Day and DateA simple indicator that show day and date at the start of each day. This is usefull in case you are downloading charts or get confused when studying past charts for expiry and non expiry day actions.
Indicators and strategies
Random Coin Toss Strategy📌 Overview
This strategy is a probability-based trading simulation that randomly decides trade direction using a coin-toss mechanism and executes trades with a customizable risk-reward ratio. It's designed primarily for testing entry frequency and risk dynamics, not predictive accuracy.
🎯 Core Concept
Every N bars (configurable), the strategy performs a pseudo-random coin toss.
Based on the result:
If heads → Buy
If tails → Sell
Once a position is opened, it sets a Stop-Loss (SL) and Take-Profit (TP) based on a multiple of the current ATR (Average True Range) value.
⚙️ Configurable Inputs
ATR Length Period for ATR calculation, determines volatility basis.
SL Multiplier SL distance = ATR × multiplier (e.g., 1.0 means 1x ATR) .
TP Multiplier TP distance = ATR × multiplier (e.g., 2.0 = 2x ATR) .
Entry Frequency Bars to wait between each new coin toss decision.
Show TP/SL Zones Toggle on/off for drawing visual TP and SL zones.
Box Size Number of bars used to define the width of the TP/SL boxes.
🔁 Entry & Exit Logic
Entry:
Happens only when no current position exists and it's the correct bar interval.
Entry direction is randomly decided.
Exit:
Positions exit at either:
Take-Profit (TP) level
Stop-Loss (SL) level
Both are calculated using the configured ATR-based distances.
🖼️ Visual Features
TP and SL zones:
Rendered as shaded rectangles (boxes) only once per trade.
Green box for TP zone, red box for SL zone.
Automatically deleted and redrawn for each new trade to avoid chart clutter.
ATR Display Table:
A minimal info table at the top-right shows the current ATR value.
Updates every few bars for performance.
🧪 Use Cases
Ideal for risk-reward modeling, strategy prototyping, and understanding how volatility-based SL/TP behavior affects results.
Great for backtesting frequency, RR tweaks (e.g., 2:5 or 3:1), and execution structure in random conditions.
⚠️ Disclaimer
Since the trade direction is random, this script is not meant for predictive trading but serves as a powerful experiment framework for studying how SL, TP, and volatility interact with random chance in a controlled, repeatable system.
Normalized Reserve Risk (Proxy Z-Score)normalised version of the reserve risk indicator on btc magazine because the btc magazine one is poo .
minchang volume tradingCondition
Point color
Volume ≥ 3× MA(24)
Violet
Volume ≥ 1.5× MA(24)
Red
Volume < 1.5× MA(24) & bullish
White
Volume < 1.5× MA(24) & bearish
Black
Nến Tô Màu Theo Volume / MA(21)Condition
Point color
Volume ≥ 3× MA(24)
Violet
Volume ≥ 1.5× MA(24)
Red
Volume < 1.5× MA(24) & bullish
White
Volume < 1.5× MA(24) & bearish
Black
AV BTC Investor ToolThe Investor Tool
Created by Philip Swift . Intended to be used by long term investors . The tool uses two simple moving averages of price as the basis for under/overvalued conditions: the 2-year MA (green) and a 5x multiple of the 2-year MA (red).
Price below the 2-year average: often means good profits and a bear market bottom .
Price above the 5x average: usually shows a bull market top , so investors may want to be cautious.
liq depth fvg/bprA script that draws liquidity depth boxes from the 9.30-10.00 am range which can prove decent areas to look for a reversal. It also draws in fvg and bpr levels which can help add confluence to a trade ideas. The 9.30 to 10.00 am range is highlighted by blue lines to assist in opening range trades as described by Casper SMC.
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FULLY FUNCTIONAL INDICATOR TESTER🎯 Purpose:
A comprehensive strategy testing framework designed to evaluate custom indicators and trading signals with professional-grade risk management and signal detection capabilities.
✨ Key Features:
Multiple Signal Detection Methods - Value changes, crossovers, threshold-based triggers
Advanced Confluence Filtering - Multi-source confirmation system with lookback periods
Professional Risk Management - Static TP/SL, break-even functionality, position sizing
Custom Exit Signals - Independent exit logic for refined strategy testing
Visual Feedback System - Clear signal plots and real-time status monitoring
Flexible Input Sources - Connect any custom indicator or built-in study
🔧 How to Use:
Connect your indicator outputs to the Entry/Exit source inputs
Select appropriate signal detection method for your indicator type
Configure risk parameters (TP/SL/Break-even)
Enable confluence filters if needed for additional confirmation
Backtest and analyze results with built-in performance metrics
📈 Signal Detection Options:
Value Change: Detects when indicator values change
Crossover Above/Below: Traditional crossover signals
Threshold Triggers: Value-based entry/exit levels
⚙️ Technical Specifications:
Compatible with Pine Script v6
Overlay strategy with position tracking
Real-time performance monitoring table
Configurable margin requirements
Full backtesting compatibility
⚠️ Important Notes:
This is a testing framework - not financial advice
Always validate signals in demo environment first
Past performance does not guarantee future results
Use proper risk management in live trading
🔄 Updates:
Enhanced signal detection algorithms
Improved confluence logic
Added break-even functionality
Visual debugging tools
Perfect for traders and developers looking to systematically tes
My script//@version=5
indicator("MA + OI + Volume Breakout", overlay=true)
// === MA Parameters ===
ma_type = input.string("EMA", title="MA Type", options= )
ma(src, len, type) =>
type == "SMA" ? ta.sma(src, len) :
type == "EMA" ? ta.ema(src, len) :
ta.wma(src, len)
ma5 = ma(close, 5, ma_type)
ma21 = ma(close, 21, ma_type)
ma50 = ma(close, 50, ma_type)
ma100 = ma(close, 100, ma_type)
plot(ma5, "5-day MA", color=color.yellow, linewidth=2)
plot(ma21, "21-day MA", color=color.orange, linewidth=2)
plot(ma50, "50-day MA", color=color.fuchsia, linewidth=2)
plot(ma100, "100-day MA", color=color.blue, linewidth=2)
// === Trend Signal ===
bullish_trend = ma5 > ma21 and ma21 > ma50 and ma50 > ma100
bearish_trend = ma5 < ma21 and ma21 < ma50 and ma50 < ma100
bgcolor(bullish_trend ? color.new(color.green, 85) : bearish_trend ? color.new(color.red, 85) : na)
// === Volume Breakout ===
vol_avg = ta.sma(volume, 20)
vol_breakout = volume > 1.5 * vol_avg
plotshape(vol_breakout, title="Volume Breakout", location=location.belowbar, style=shape.circle, color=color.aqua, size=size.tiny)
// === Open Interest Overlay (assumes OI data via external input or future integration) ===
// Placeholder: simulate OI input (replace with `request.security(syminfo.tickerid, ..., ...)` if available)
oi = input.float(na, title="Open Interest (external feed)")
oi_avg = ta.sma(oi, 20)
oi_breakout = oi > 1.2 * oi_avg
plotshape(not na(oi) and oi_breakout, title="OI Spike", location=location.belowbar, style=shape.diamond, color=color.purple, size=size.tiny)
plot(oi, title="Open Interest", color=color.gray, display=display.none) // Optional: hidden line for alerts
// === Composite Signal ===
strong_long = bullish_trend and vol_breakout and oi_breakout
plotshape(strong_long, title="Strong Long Signal", location=location.belowbar, style=shape.labelup, text="LONG", size=size.small, color=color.lime)
// === Screener Logic ===
// Use `strong_long` as your filter condition in a screener or dashboard output
Custom MTF DBoardSimple MTF Dboard to use with other indicators as a confluence.
Uses LuxAlgo's SMC concepts to show PA's trend direction- the idea is , if the trends dont align fully, dont take the trade. Or if one of the timeframes are different, maybe its time to get out of a trade cus its gonna reverse into your face?
Try it Lemme know lol
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Previous 10 Weekly Highs/Lows z s s bsf bsfd sfdv svdvvdsfvsdvsddvbadvvf zfvdzcxvdsfzv dfcvfdcxvsfdzvzdsfcx
Momentum SNR VIP [3 TP + Max 50 Pip SL]//@version=6
indicator("Momentum SNR VIP ", overlay=true)
// === Settings ===
pip = input.float(0.0001, "Pip Size", step=0.0001)
sl_pip = 50 * pip
tp1_pip = 40 * pip
tp2_pip = 70 * pip
tp3_pip = 100 * pip
lookback = input.int(20, "Lookback for S/R", minval=5)
// === SNR ===
pivotHigh = ta.pivothigh(high, lookback, lookback)
pivotLow = ta.pivotlow(low, lookback, lookback)
supportZone = not na(pivotLow)
resistanceZone = not na(pivotHigh)
plotshape(supportZone, title="Support", location=location.belowbar, color=color.blue, style=shape.triangleup, size=size.tiny)
plotshape(resistanceZone, title="Resistance", location=location.abovebar, color=color.red, style=shape.triangledown, size=size.tiny)
// === Price Action ===
bullishEngulfing = close < open and close > open and close > open and open <= close
bearishEngulfing = close > open and close < open and close < open and open >= close
bullishPinBar = close < open and (low - math.min(open, close)) > 1.5 * math.abs(close - open)
bearishPinBar = close > open and (high - math.max(open, close)) > 1.5 * math.abs(close - open)
buySignal = supportZone and (bullishEngulfing or bullishPinBar)
sellSignal = resistanceZone and (bearishEngulfing or bearishPinBar)
// === SL & TP ===
rawBuySL = low - 10 * pip
buySL = math.max(close - sl_pip, rawBuySL)
buyTP1 = close + tp1_pip
buyTP2 = close + tp2_pip
buyTP3 = close + tp3_pip
rawSellSL = high + 10 * pip
sellSL = math.min(close + sl_pip, rawSellSL)
sellTP1 = close - tp1_pip
sellTP2 = close - tp2_pip
sellTP3 = close - tp3_pip
// === Plot Lines ===
plot(buySignal ? buySL : na, title="Buy SL", color=color.red, style=plot.style_line, linewidth=1)
plot(buySignal ? buyTP1 : na, title="Buy TP1", color=color.green, style=plot.style_line, linewidth=1)
plot(buySignal ? buyTP2 : na, title="Buy TP2", color=color.green, style=plot.style_line, linewidth=1)
plot(buySignal ? buyTP3 : na, title="Buy TP3", color=color.green, style=plot.style_line, linewidth=1)
plot(sellSignal ? sellSL : na, title="Sell SL", color=color.red, style=plot.style_line, linewidth=1)
plot(sellSignal ? sellTP1 : na, title="Sell TP1", color=color.green, style=plot.style_line, linewidth=1)
plot(sellSignal ? sellTP2 : na, title="Sell TP2", color=color.green, style=plot.style_line, linewidth=1)
plot(sellSignal ? sellTP3 : na, title="Sell TP3", color=color.green, style=plot.style_line, linewidth=1)
// === Floating Labels on Right Side ===
if buySignal
label.new(x=bar_index + 50, y=buySL, text="SL", style=label.style_label_right, color=color.red, textcolor=color.white)
label.new(x=bar_index + 50, y=buyTP1, text="TP1", style=label.style_label_right, color=color.green, textcolor=color.white)
label.new(x=bar_index + 50, y=buyTP2, text="TP2", style=label.style_label_right, color=color.green, textcolor=color.white)
label.new(x=bar_index + 50, y=buyTP3, text="TP3", style=label.style_label_right, color=color.green, textcolor=color.white)
if sellSignal
label.new(x=bar_index + 50, y=sellSL, text="SL", style=label.style_label_right, color=color.red, textcolor=color.white)
label.new(x=bar_index + 50, y=sellTP1, text="TP1", style=label.style_label_right, color=color.green, textcolor=color.white)
label.new(x=bar_index + 50, y=sellTP2, text="TP2", style=label.style_label_right, color=color.green, textcolor=color.white)
label.new(x=bar_index + 50, y=sellTP3, text="TP3", style=label.style_label_right, color=color.green, textcolor=color.white)
// === Signal Markers ===
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")
// === Alerts ===
alertcondition(buySignal, title="Buy Alert", message="🟢 BUY at Support Zone + Price Action")
alertcondition(sellSignal, title="Sell Alert", message="🟡 SELL at Resistance Zone + Price Action")
VWAP SlopePositive (green) bars mean today’s (or this interval’s) VWAP is higher than the prior one → volume‐weighted average price is drifting up → bullish flow.
Negative (red) bars mean VWAP is lower than before → volume is skewed to sellers → bearish flow.
Bar height shows how much VWAP has shifted, so taller bars = stronger conviction.
Why it’s useful:
It gives you a real-time read on whether institutions are consistently buying at higher prices or selling at lower prices.
Use it as a bias filter: for shorts you want to see red bars (VWAP down-slope) at your entry, and for longs green bars (VWAP up-slope).
Because it updates tick-by-tick (or per bar), you get a live snapshot of volume-weighted momentum on top of your price‐action and oscillator signals.
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Momentum SNR VIP [3 TP + Max 50 Pip SL]//@version=6
indicator("Momentum SNR VIP ", overlay=true)
// === Settings ===
pip = input.float(0.0001, "Pip Size", step=0.0001)
sl_pip = 50 * pip
tp1_pip = 40 * pip
tp2_pip = 70 * pip
tp3_pip = 100 * pip
lookback = input.int(20, "Lookback for S/R", minval=5)
// === SNR ===
pivotHigh = ta.pivothigh(high, lookback, lookback)
pivotLow = ta.pivotlow(low, lookback, lookback)
supportZone = not na(pivotLow)
resistanceZone = not na(pivotHigh)
plotshape(supportZone, title="Support", location=location.belowbar, color=color.blue, style=shape.triangleup, size=size.tiny)
plotshape(resistanceZone, title="Resistance", location=location.abovebar, color=color.red, style=shape.triangledown, size=size.tiny)
// === Price Action ===
bullishEngulfing = close < open and close > open and close > open and open <= close
bearishEngulfing = close > open and close < open and close < open and open >= close
bullishPinBar = close < open and (low - math.min(open, close)) > 1.5 * math.abs(close - open)
bearishPinBar = close > open and (high - math.max(open, close)) > 1.5 * math.abs(close - open)
buySignal = supportZone and (bullishEngulfing or bullishPinBar)
sellSignal = resistanceZone and (bearishEngulfing or bearishPinBar)
// === SL & TP ===
rawBuySL = low - 10 * pip
buySL = math.max(close - sl_pip, rawBuySL)
buyTP1 = close + tp1_pip
buyTP2 = close + tp2_pip
buyTP3 = close + tp3_pip
rawSellSL = high + 10 * pip
sellSL = math.min(close + sl_pip, rawSellSL)
sellTP1 = close - tp1_pip
sellTP2 = close - tp2_pip
sellTP3 = close - tp3_pip
// === Plot Buy/Sell Signal
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")
// === Plot SL & TP lines
plot(buySignal ? buySL : na, title="Buy SL", color=color.red, style=plot.style_linebr, linewidth=1)
plot(buySignal ? buyTP1 : na, title="Buy TP1", color=color.green, style=plot.style_linebr, linewidth=1)
plot(buySignal ? buyTP2 : na, title="Buy TP2", color=color.green, style=plot.style_linebr, linewidth=1)
plot(buySignal ? buyTP3 : na, title="Buy TP3", color=color.green, style=plot.style_linebr, linewidth=1)
plot(sellSignal ? sellSL : na, title="Sell SL", color=color.red, style=plot.style_linebr, linewidth=1)
plot(sellSignal ? sellTP1 : na, title="Sell TP1", color=color.green, style=plot.style_linebr, linewidth=1)
plot(sellSignal ? sellTP2 : na, title="Sell TP2", color=color.green, style=plot.style_linebr, linewidth=1)
plot(sellSignal ? sellTP3 : na, title="Sell TP3", color=color.green, style=plot.style_linebr, linewidth=1)
// === Labels
if buySignal
label.new(x=bar_index, y=buySL, text="SL : " + str.tostring(buySL, "#.0000"), style=label.style_label_down, color=color.red, textcolor=color.white)
label.new(x=bar_index, y=buyTP1, text="TP1 : " + str.tostring(buyTP1, "#.0000"), style=label.style_label_up, color=color.green, textcolor=color.white)
label.new(x=bar_index, y=buyTP2, text="TP2 : " + str.tostring(buyTP2, "#.0000"), style=label.style_label_up, color=color.green, textcolor=color.white)
label.new(x=bar_index, y=buyTP3, text="TP3 : " + str.tostring(buyTP3, "#.0000"), style=label.style_label_up, color=color.green, textcolor=color.white)
if sellSignal
label.new(x=bar_index, y=sellSL, text="SL : " + str.tostring(sellSL, "#.0000"), style=label.style_label_up, color=color.red, textcolor=color.white)
label.new(x=bar_index, y=sellTP1, text="TP1 : " + str.tostring(sellTP1, "#.0000"), style=label.style_label_down, color=color.green, textcolor=color.white)
label.new(x=bar_index, y=sellTP2, text="TP2 : " + str.tostring(sellTP2, "#.0000"), style=label.style_label_down, color=color.green, textcolor=color.white)
label.new(x=bar_index, y=sellTP3, text="TP3 : " + str.tostring(sellTP3, "#.0000"), style=label.style_label_down, color=color.green, textcolor=color.white)
// === Alerts
alertcondition(buySignal, title="Buy Alert", message="🟢 BUY at Support Zone + Price Action")
alertcondition(sellSignal, title="Sell Alert", message="🟡 SELL at Resistance Zone + Price Action")
Momentum SNR VIP [INDICATOR ONLY]//@version=6
indicator("Momentum SNR VIP ", overlay=true)
// === Inputs ===
lookback = input.int(20, "Lookback for S/R", minval=5)
rr_ratio = input.float(2.0, "Risk-Reward Ratio", minval=0.5, step=0.1)
// === SNR Detection ===
pivotHigh = ta.pivothigh(high, lookback, lookback)
pivotLow = ta.pivotlow(low, lookback, lookback)
supportZone = not na(pivotLow)
resistanceZone = not na(pivotHigh)
plotshape(supportZone, title="Support", location=location.belowbar, color=color.blue, style=shape.triangleup, size=size.tiny)
plotshape(resistanceZone, title="Resistance", location=location.abovebar, color=color.red, style=shape.triangledown, size=size.tiny)
// === Price Action ===
bullishEngulfing = close < open and close > open and close > open and open <= close
bearishEngulfing = close > open and close < open and close < open and open >= close
bullishPinBar = close < open and (low - math.min(open, close)) > 1.5 * math.abs(close - open)
bearishPinBar = close > open and (high - math.max(open, close)) > 1.5 * math.abs(close - open)
buySignal = supportZone and (bullishEngulfing or bullishPinBar)
sellSignal = resistanceZone and (bearishEngulfing or bearishPinBar)
// === SL & TP ===
buySL = low - 10
buyTP = close + (close - buySL) * rr_ratio
sellSL = high + 10
sellTP = close - (sellSL - close) * rr_ratio
// === Plot Signals
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")
plot(buySignal ? buySL : na, title="Buy SL", color=color.red, style=plot.style_linebr, linewidth=1)
plot(buySignal ? buyTP : na, title="Buy TP", color=color.green, style=plot.style_linebr, linewidth=1)
plot(sellSignal ? sellSL : na, title="Sell SL", color=color.red, style=plot.style_linebr, linewidth=1)
plot(sellSignal ? sellTP : na, title="Sell TP", color=color.green, style=plot.style_linebr, linewidth=1)
// === Labels (Fixed)
if buySignal
label.new(x=bar_index, y=buySL, text="SL : " + str.tostring(buySL, "#.00"), style=label.style_label_down, color=color.red, textcolor=color.white)
label.new(x=bar_index, y=buyTP, text="TP 1 : " + str.tostring(buyTP, "#.00"), style=label.style_label_up, color=color.green, textcolor=color.white)
if sellSignal
label.new(x=bar_index, y=sellSL, text="SL : " + str.tostring(sellSL, "#.00"), style=label.style_label_up, color=color.red, textcolor=color.white)
label.new(x=bar_index, y=sellTP, text="TP 1 : " + str.tostring(sellTP, "#.00"), style=label.style_label_down, color=color.green, textcolor=color.white)
// === Alerts
alertcondition(buySignal, title="Buy Alert", message="🟢 BUY at Support Zone + Price Action")
alertcondition(sellSignal, title="Sell Alert", message="🟡 SELL at Resistance Zone + Price Action")
Repeating Trend HighlighterThis custom indicator helps you see when the current price trend is similar to a past trend over the same number of candles. Think of it like checking whether the market is repeating itself.
You choose three settings:
• Lookback Period: This is how many candles you want to measure. For example, if you set it to 10, it looks at the price change over the last 10 bars.
• Offset Bars Ago: This tells the indicator how far back in time to look for a similar move. If you set it to 50, it compares the current move to what happened 50 bars earlier.
• Tolerance (%): This is how closely the moves must match to be considered similar. A smaller number means you only get a signal if the moves are almost the same, while a larger number allows more flexibility.
When the current price move is close enough to the past move you picked, the background of your chart turns light green. This makes it easy to spot repeating trends without studying numbers manually.
You’ll also see two lines under your chart if you enable them: a blue line showing the percentage change of the current move and an orange line showing the change in the past move. These help you compare visually.
This tool is useful in several ways. You can use it to confirm your trading setups, for example if you suspect that a strong rally or pullback is happening again. You can also use it to filter trades by combining it with other indicators, so you only enter when trends repeat. Many traders use it as a learning tool, experimenting with different lookback periods and offsets to understand how often similar moves happen.
If you are a scalper working on short timeframes, you can set the lookback to a small number like 3–5 bars. Swing traders who prefer daily or weekly charts might use longer lookbacks like 20–30 bars.
Keep in mind that this indicator doesn’t guarantee price will move the same way again—it only shows similarity in how price changed over time. It works best when you use it together with other signals or market context.
In short, it’s like having a simple spotlight that tells you: “This move looks a lot like what happened before.” You can then decide if you want to act on that information.
If you’d like, I can help you tweak the settings or combine it with alerts so it notifies you when these patterns appear.
EVaR Indicator and Position SizingThe Problem:
Financial markets consistently show "fat-tailed" distributions where extreme events occur with higher frequency than predicted by normal distributions (Gaussian or even log-normal). These fat tails manifest in sudden price crashes, volatility spikes, and black swan events that traditional risk measures like volatility can underestimate. Standard deviation and conventional VaR calculations assume normally distributed returns, leaving traders vulnerable to severe drawdowns during market stress.
Cryptocurrencies and volatile instruments display particularly pronounced fat-tailed behavior, with extreme moves occurring 5-10 times more frequently than normal distribution models would predict. This reality demands a more sophisticated approach to risk measurement and position sizing.
The Solution: Entropic Value at Risk (EVAR)
EVaR addresses these limitations by incorporating principles from statistical mechanics and information theory through Tsallis entropy. This advanced approach captures the non-linear dependencies and power-law distributions characteristic of real financial markets.
Entropy is more adaptive than standard deviations and volatility measures.
I was inspired to create this indicator after reading the paper " The End of Mean-Variance? Tsallis Entropy Revolutionises Portfolio Optimisation in Cryptocurrencies " by by Sana Gaied Chortane and Kamel Naoui.
Key advantages of EVAR over traditional risk measures:
Superior tail risk capture: More accurately quantifies the probability of extreme market moves
Adaptability to market regimes: Self-calibrates to changing volatility environments
Non-parametric flexibility: Makes less assumptions about the underlying return distribution
Forward-looking risk assessment: Better anticipates potential market changes (just look at the charts :)
Mathematically, EVAR is defined as:
EVAR_α(X) = inf_{z>0} {z * log(1/α * M_X(1/z))}
Where the moment-generating function is calculated using q-exponentials rather than conventional exponentials, allowing precise modeling of fat-tailed behavior.
Technical Implementation
This indicator implements EVAR through a q-exponential approach from Tsallis statistics:
Returns Calculation: Price returns are calculated over the lookback period
Moment Generating Function: Approximated using q-exponentials to account for fat tails
EVAR Computation: Derived from the MGF and confidence parameter
Normalization: Scaled to for intuitive visualization
Position Sizing: Inversely modulated based on normalized EVAR
The q-parameter controls tail sensitivity—higher values (1.5-2.0) increase the weighting of extreme events in the calculation, making the model more conservative during potentially turbulent conditions.
Indicator Components
1. EVAR Risk Visualization
Dynamic EVAR Plot: Color-coded from red to green normalized risk measurement (0-1)
Risk Thresholds: Reference lines at 0.3, 0.5, and 0.7 delineating risk zones
2. Position Sizing Matrix
Risk Assessment: Current risk level and raw EVAR value
Position Recommendations: Percentage allocation, dollar value, and quantity
Stop Parameters: Mathematically derived stop price with percentage distance
Drawdown Projection: Maximum theoretical loss if stop is triggered
Interpretation and Application
The normalized EVAR reading provides a probabilistic risk assessment:
< 0.3: Low risk environment with minimal tail concerns
0.3-0.5: Moderate risk with standard tail behavior
0.5-0.7: Elevated risk with increased probability of significant moves
> 0.7: High risk environment with substantial tail risk present
Position sizing is automatically calculated using an inverse relationship to EVAR, contracting during high-risk periods and expanding during low-risk conditions. This is a counter-cyclical approach that ensures consistent risk exposure across varying market regimes, especially when the market is hyped or overheated.
Parameter Optimization
For optimal risk assessment across market conditions:
Lookback Period: Determines the historical window for risk calculation
Q Parameter: Controls tail sensitivity (higher values increase conservatism)
Confidence Level: Sets the statistical threshold for risk assessment
For cryptocurrencies and highly volatile instruments, a q-parameter between 1.5-2.0 typically provides the most accurate risk assessment because it helps capturing the fat-tailed behavior characteristic of these markets. You can also increase the q-parameter for more conservative approaches.
Practical Applications
Adaptive Risk Management: Quantify and respond to changing tail risk conditions
Volatility-Normalized Positioning: Maintain consistent exposure across market regimes
Black Swan Detection: Early identification of potential extreme market conditions
Portfolio Construction: Apply consistent risk-based sizing across diverse instruments
This indicator is my own approach to entropy-based risk measures as an alterative to volatility and standard deviations and it helps with fat-tailed markets.
Enjoy!