TickAtlas
Advanced ~45 min

Backtest Your Strategy

Use historical indicator data to validate your strategies before risking real capital. This guide covers building a simple backtesting framework with Python.

Simple RSI Backtest

This example tests a mean-reversion strategy: buy when RSI drops below 30, sell when it recovers above 50.

python 
import requests
from datetime import datetime

API_KEY = "YOUR_API_KEY"
BASE = "https://tickatlas.com/v1"
headers = {"X-API-Key": API_KEY}

def get_historical(symbol, indicator, timeframe, bars=500):
    """Fetch historical indicator values."""
    params = {
        "symbol": symbol, "indicator": indicator,
        "timeframe": timeframe, "bars": bars
    }
    return requests.get(f"{BASE}/indicator",
        headers=headers, params=params).json()

def backtest_rsi(symbol, timeframe="H1", bars=500):
    """Simple RSI mean-reversion backtest."""
    data = get_historical(symbol, "RSI_14", timeframe, bars)
    history = data.get("history", [])

    trades = []
    position = None

    for i, bar in enumerate(history):
        rsi = bar["value"]
        price = bar["ohlc"]["close"]

        if position is None and rsi < 30:
            position = {"entry": price, "entry_rsi": rsi, "bar": i}
        elif position and rsi > 50:
            pnl = price - position["entry"]
            trades.append({
                "entry": position["entry"], "exit": price,
                "pnl_pips": pnl * 10000, "bars_held": i - position["bar"]
            })
            position = None

    wins = [t for t in trades if t["pnl_pips"] > 0]
    total = len(trades)
    win_rate = len(wins) / total * 100 if total else 0
    avg_pnl = sum(t["pnl_pips"] for t in trades) / total if total else 0

    print(f"Symbol: {symbol} | Timeframe: {timeframe}")
    print(f"Total trades: {total} | Win rate: {win_rate:.1f}%")
    print(f"Avg PnL: {avg_pnl:.1f} pips")
    return trades

# Run backtest
trades = backtest_rsi("EURUSD", "H1", 500)

Key Metrics to Track

Win Rate

Percentage of profitable trades. Above 50% is good for mean-reversion.

Profit Factor

Gross profit / gross loss. Above 1.5 indicates a viable strategy.

Max Drawdown

Largest peak-to-trough decline. Keep below 20% of account.

Sharpe Ratio

Risk-adjusted return. Above 1.0 is acceptable; above 2.0 is excellent.

Best Practices

  • Test across multiple symbols and timeframes to avoid curve-fitting
  • Use out-of-sample data for final validation (don't optimize on all data)
  • Account for spread and slippage in your PnL calculations
  • Include transaction costs in profitability analysis
  • Test in both trending and ranging market conditions

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