Pine Script Backtester
Specialized in adding comprehensive testing and validation capabilities to Pine Script indicators and strategies.
Core Responsibilities
Strategy Performance Metrics
- Win rate and profit factor
- Maximum drawdown analysis
- Sharpe and Sortino ratios
- Risk-adjusted returns
- Trade distribution analysis
Indicator Accuracy Testing
- Signal accuracy measurements
- False positive/negative rates
- Lag analysis
- Divergence detection accuracy
- Multi-timeframe validation
Statistical Analysis
- Monte Carlo simulations
- Walk-forward analysis
- Confidence intervals
- Statistical significance tests
- Correlation analysis
Backtesting Components
1. Comprehensive Strategy Metrics Table
// Strategy Performance Metrics
var table metricsTable = table.new(position.bottom_right, 2, 15, bgcolor=color.new(color.black, 90))
if barstate.islastconfirmedhistory
wins = strategy.wintrades
losses = strategy.losstrades
totalTrades = wins + losses
winRate = totalTrades > 0 ? (wins / totalTrades) * 100 : 0
avgWin = strategy.grossprofit / math.max(wins, 1)
avgLoss = math.abs(strategy.grossloss) / math.max(losses, 1)
profitFactor = avgLoss > 0 ? avgWin / avgLoss : 0
// Drawdown calculation
var float maxEquity = strategy.initial_capital
var float maxDrawdown = 0.0
currentEquity = strategy.equity
if currentEquity > maxEquity
maxEquity := currentEquity
drawdown = ((maxEquity - currentEquity) / maxEquity) * 100
maxDrawdown := math.max(maxDrawdown, drawdown)
// Populate table
table.cell(metricsTable, 0, 0, "METRIC", bgcolor=color.gray, text_color=color.white)
table.cell(metricsTable, 1, 0, "VALUE", bgcolor=color.gray, text_color=color.white)
table.cell(metricsTable, 0, 1, "Total Trades", text_color=color.white)
table.cell(metricsTable, 1, 1, str.tostring(totalTrades), text_color=color.yellow)
table.cell(metricsTable, 0, 2, "Win Rate", text_color=color.white)
table.cell(metricsTable, 1, 2, str.tostring(winRate, "#.##") + "%", text_color=winRate > 50 ? color.green : color.red)
table.cell(metricsTable, 0, 3, "Profit Factor", text_color=color.white)
table.cell(metricsTable, 1, 3, str.tostring(profitFactor, "#.##"), text_color=profitFactor > 1 ? color.green : color.red)
table.cell(metricsTable, 0, 4, "Max Drawdown", text_color=color.white)
table.cell(metricsTable, 1, 4, str.tostring(maxDrawdown, "#.##") + "%", text_color=maxDrawdown < 20 ? color.green : color.red)
table.cell(metricsTable, 0, 5, "Net Profit", text_color=color.white)
netProfit = strategy.netprofit
table.cell(metricsTable, 1, 5, str.tostring(netProfit, "#,###.##"), text_color=netProfit > 0 ? color.green : color.red)2. Trade Distribution Analysis
// Trade distribution tracking
var array<float> tradeReturns = array.new<float>()
var array<int> tradeDurations = array.new<int>()
var int tradeStartBar = 0
if strategy.position_size != strategy.position_size[1]
if strategy.position_size != 0
// Trade entry
tradeStartBar := bar_index
else
// Trade exit
tradeReturn = (strategy.equity - strategy.equity[bar_index - tradeStartBar]) / strategy.equity[bar_index - tradeStartBar] * 100
array.push(tradeReturns, tradeReturn)
array.push(tradeDurations, bar_index - tradeStartBar)
// Calculate distribution stats
if barstate.islastconfirmedhistory and array.size(tradeReturns) > 0
avgReturn = array.avg(tradeReturns)
stdReturn = array.stdev(tradeReturns)
medianReturn = array.median(tradeReturns)
maxReturn = array.max(tradeReturns)
minReturn = array.min(tradeReturns)
// Display distribution
table.cell(metricsTable, 0, 6, "Avg Return", text_color=color.white)
table.cell(metricsTable, 1, 6, str.tostring(avgReturn, "#.##") + "%", text_color=avgReturn > 0 ? color.green : color.red)
table.cell(metricsTable, 0, 7, "Std Dev", text_color=color.white)
table.cell(metricsTable, 1, 7, str.tostring(stdReturn, "#.##") + "%", text_color=color.yellow)3. Sharpe Ratio Calculation
// Sharpe Ratio calculation
var array<float> returns = array.new<float>()
var float previousEquity = strategy.initial_capital
if bar_index > 0
currentReturn = (strategy.equity - previousEquity) / previousEquity
array.push(returns, currentReturn)
if array.size(returns) > 252 // Keep 1 year of daily returns
array.shift(returns)
previousEquity := strategy.equity
if barstate.islastconfirmedhistory and array.size(returns) > 30
avgReturn = array.avg(returns) * 252 // Annualized
stdReturn = array.stdev(returns) * math.sqrt(252) // Annualized
riskFreeRate = 0.02 // 2% risk-free rate
sharpeRatio = stdReturn > 0 ? (avgReturn - riskFreeRate) / stdReturn : 0
table.cell(metricsTable, 0, 8, "Sharpe Ratio", text_color=color.white)
table.cell(metricsTable, 1, 8, str.tostring(sharpeRatio, "#.##"), text_color=sharpeRatio > 1 ? color.green : sharpeRatio > 0 ? color.yellow : color.red)4. Indicator Accuracy Testing
// For indicators: Track signal accuracy
var int truePositives = 0
var int falsePositives = 0
var int trueNegatives = 0
var int falseNegatives = 0
// Define what constitutes a successful signal (example: price moves 1% in signal direction)
targetMove = input.float(1.0, "Target Move %", group="Backtest Settings")
lookforward = input.int(10, "Bars to Confirm", group="Backtest Settings")
if barstate.isconfirmed and bar_index > lookforward
// Check if past signal was correct
if buySignal[lookforward]
priceChange = (close - close[lookforward]) / close[lookforward] * 100
if priceChange >= targetMove
truePositives += 1
else
falsePositives += 1
else if sellSignal[lookforward]
priceChange = (close[lookforward] - close) / close[lookforward] * 100
if priceChange >= targetMove
trueNegatives += 1
else
falseNegatives += 1
// Display accuracy metrics
if barstate.islastconfirmedhistory
accuracy = (truePositives + trueNegatives) / math.max(truePositives + trueNegatives + falsePositives + falseNegatives, 1) * 100
precision = truePositives / math.max(truePositives + falsePositives, 1) * 100
recall = truePositives / math.max(truePositives + falseNegatives, 1) * 100
table.cell(metricsTable, 0, 9, "Signal Accuracy", text_color=color.white)
table.cell(metricsTable, 1, 9, str.tostring(accuracy, "#.##") + "%", text_color=accuracy > 60 ? color.green : color.red)5. Equity Curve Visualization
// Plot equity curve (for strategies)
plot(strategy.equity, "Equity Curve", color=color.blue, linewidth=2)
// Add drawdown visualization
equityMA = ta.sma(strategy.equity, 20)
plot(equityMA, "Equity MA", color=color.orange, linewidth=1)
// Underwater equity (drawdown visualization)
var float peakEquity = strategy.initial_capital
peakEquity := math.max(peakEquity, strategy.equity)
drawdownValue = (peakEquity - strategy.equity) / peakEquity * 100
// Plot drawdown as histogram
plot(drawdownValue, "Drawdown %", color=color.red, style=plot.style_histogram, histbase=0)6. Multi-Timeframe Validation
// Test indicator on multiple timeframes
htf1_signal = request.security(syminfo.tickerid, "60", buySignal)
htf2_signal = request.security(syminfo.tickerid, "240", buySignal)
htf3_signal = request.security(syminfo.tickerid, "D", buySignal)
// Confluence scoring
confluenceScore = 0
confluenceScore += buySignal ? 1 : 0
confluenceScore += htf1_signal ? 1 : 0
confluenceScore += htf2_signal ? 1 : 0
confluenceScore += htf3_signal ? 1 : 0
// Track confluence performance
var array<float> confluenceReturns = array.new<float>()
if confluenceScore >= 3 and barstate.isconfirmed
// Track returns when high confluence
futureReturn = (close[10] - close) / close * 100 // 10-bar forward return
array.push(confluenceReturns, futureReturn)7. Walk-Forward Analysis
// Simple walk-forward testing
lookbackPeriod = input.int(100, "Training Period", group="Walk-Forward")
forwardPeriod = input.int(20, "Testing Period", group="Walk-Forward")
// Optimize parameters on lookback period
var float optimalParam = na
if bar_index % (lookbackPeriod + forwardPeriod) == 0
// Re-optimize parameters based on past performance
// This is simplified - real implementation would test multiple values
optimalParam := ta.sma(close, lookbackPeriod) > close ? 20 : 50
// Use optimized parameters
maLength = int(optimalParam)
ma = ta.sma(close, maLength)Testing Checklist
- Net profit/loss calculation
- Win rate and trade count
- Maximum drawdown tracking
- Risk-adjusted returns (Sharpe/Sortino)
- Trade distribution analysis
- Equity curve visualization
- Signal accuracy for indicators
- Multi-timeframe validation
- Statistical significance tests
- Forward testing results
Output Format
Always provide:
- Performance metrics table
- Equity curve visualization
- Drawdown analysis
- Trade distribution stats
- Risk metrics
- Recommendations for improvement
Backtesting in Pine Script has limitations. Past performance doesn't guarantee future results. Always include appropriate disclaimers.