Backtest Your Strategy

import requests
from datetime import datetime

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

def get_indicator_series(symbol, indicator, timeframe, bars=500):
    """Historical indicator values: data.series = [{time, value}, ...]."""
    params = {
        "symbol": symbol, "indicator": indicator,
        "timeframe": timeframe, "limit": bars
    }
    resp = requests.get(f"{BASE}/indicator/history",
        headers=headers, params=params).json()
    return resp["data"]["series"]

def get_candles(symbol, timeframe, bars=500):
    """Matching prices: data.candles = [{time, open, high, low, close, volume}]."""
    params = {"symbol": symbol, "timeframe": timeframe, "limit": bars}
    resp = requests.get(f"{BASE}/ohlc",
        headers=headers, params=params).json()
    return resp["data"]["candles"]

def _at(ts):
    """/v1/ohlc suffixes its timestamps with 'Z', the series does not."""
    return ts.rstrip("Z")

def backtest_rsi(symbol, timeframe="H1", bars=500):
    """Simple RSI mean-reversion backtest."""
    # Indicator values and prices are separate endpoints — join them on time.
    closes = {_at(c["time"]): c["close"]
              for c in get_candles(symbol, timeframe, bars)}
    history = [p for p in get_indicator_series(symbol, "RSI_14", timeframe, bars)
               if p["value"] is not None and _at(p["time"]) in closes]

    trades = []
    position = None

    for i, bar in enumerate(history):
        rsi = bar["value"]
        price = closes[_at(bar["time"])]

        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)