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.