Backtest Strategies with Real Historical Data

import requests

API_KEY = "YOUR_API_KEY"
BASE = "https://tickatlas.com"

def get_ohlc(symbol, timeframe, bars=200):
    resp = requests.get(
        f"{BASE}/v1/ohlc",
        headers={"X-API-Key": API_KEY},
        params={"symbol": symbol, "timeframe": timeframe, "bars": bars}
    )
    return resp.json()["data"]["bars"]

def get_indicators(symbol, timeframe, indicator, period):
    resp = requests.get(
        f"{BASE}/v1/indicators",
        headers={"X-API-Key": API_KEY},
        params={"symbol": symbol, "timeframe": timeframe,
                "indicator": indicator, "period": period, "bars": 200}
    )
    return resp.json()["data"]["values"]

# Fetch data
bars = get_ohlc("EURUSD", "H1", 200)
sma_20 = get_indicators("EURUSD", "H1", "sma", 20)
sma_50 = get_indicators("EURUSD", "H1", "sma", 50)

# Simple SMA crossover backtest
trades = []
position = None

for i in range(1, len(sma_20)):
    if sma_20[i] > sma_50[i] and sma_20[i-1] <= sma_50[i-1]:
        # Golden cross - BUY
        if position is None:
            position = {"type": "BUY", "entry": bars[i]["close"],
                        "time": bars[i]["time"]}
    elif sma_20[i] < sma_50[i] and sma_20[i-1] >= sma_50[i-1]:
        # Death cross - SELL / close position
        if position and position["type"] == "BUY":
            pnl = bars[i]["close"] - position["entry"]
            trades.append({**position, "exit": bars[i]["close"],
                          "pnl_pips": pnl * 10000})
            position = None

# Results
total_pnl = sum(t["pnl_pips"] for t in trades)
wins = sum(1 for t in trades if t["pnl_pips"] > 0)
print(f"Trades: {len(trades)} | Wins: {wins} | "
      f"Win Rate: {wins/len(trades)*100:.0f}% | "
      f"Total PnL: {total_pnl:.1f} pips")