Initial commit: multi-symbol bot with backtest engine and RSI trend strategy
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
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#!/usr/bin/env python3
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"""
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Multi-symbol backtest.
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Tests the optimised RSI Trend strategy across every M15 pair in the database
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with at least MIN_CANDLES bars. Ranks pairs by Sharpe ratio and prints an
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aggregate portfolio equity curve.
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"""
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import sys
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).parent))
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import sqlite3
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import numpy as np
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from engine.backtest import BacktestEngine
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from engine.data import load_candles, DB_PATH
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from engine.metrics import compute
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from strategies.rsi_trend import RSITrendStrategy
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try:
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from reports.chart import save_equity_chart
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CHARTS = True
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except ImportError:
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CHARTS = False
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# ── config ────────────────────────────────────────────────────────────────────
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MIN_CANDLES = 1_000 # require ≥ 1 000 M15 bars (≈ 6 months)
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EXCLUDE = {"USD_JPY", "EUR_USD", "GBP_USD", "AUD_USD", # OANDA-format dupes
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"EUR_JPY", "GBP_JPY", "EUR_JPY"}
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# Optimised params from single-symbol grid search
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BEST_PARAMS = dict(
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os_level=30, ob_level=75, sl_atr=2.0, tp_atr=3.0,
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trend_fast=50, trend_slow=200,
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session_start=7, session_end=20,
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)
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INITIAL_BALANCE = 10_000.0
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RISK_PER_TRADE = 0.01
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SPREAD_PIPS = 0.5
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# ── helpers ───────────────────────────────────────────────────────────────────
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def available_symbols() -> list[tuple[str, int]]:
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conn = sqlite3.connect(DB_PATH)
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rows = conn.execute(
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"SELECT symbol, COUNT(*) FROM candles WHERE timeframe='M15' "
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"GROUP BY symbol ORDER BY COUNT(*) DESC"
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).fetchall()
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conn.close()
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return [(sym, cnt) for sym, cnt in rows
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if cnt >= MIN_CANDLES and sym not in EXCLUDE and "_" not in sym]
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def bar(val: float, max_val: float, width: int = 20) -> str:
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filled = int(round(abs(val) / max(abs(max_val), 1e-9) * width))
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char = "█" if val >= 0 else "▒"
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return char * filled
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# ── main ──────────────────────────────────────────────────────────────────────
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def main():
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symbols = available_symbols()
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print("=" * 72)
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print(" Multi-Symbol Backtest — RSI Trend Pullback (optimised params)")
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print("=" * 72)
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print(f" Params : {BEST_PARAMS}")
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print(f" Symbols: {len(symbols)} pairs (min {MIN_CANDLES} M15 candles)")
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print()
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engine = BacktestEngine(
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"MULTI",
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initial_balance=INITIAL_BALANCE,
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risk_per_trade=RISK_PER_TRADE,
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spread_pips=SPREAD_PIPS,
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)
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results = []
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for symbol, n_candles in symbols:
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# pip size is embedded in the engine per symbol — re-create for each
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sym_engine = BacktestEngine(
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symbol,
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initial_balance=INITIAL_BALANCE,
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risk_per_trade=RISK_PER_TRADE,
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spread_pips=SPREAD_PIPS,
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)
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df = load_candles(symbol, "M15")
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strategy = RSITrendStrategy(**BEST_PARAMS)
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trades, equity = sym_engine.run(df, strategy)
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m = compute(trades, equity, INITIAL_BALANCE)
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results.append((symbol, n_candles, m, equity))
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status = "+" if m.total_return > 0 else "-"
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print(f" [{status}] {symbol:<10} {n_candles:>6} bars "
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f"T={m.n_trades:>3} WR={m.win_rate*100:>5.1f}% "
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f"PF={m.profit_factor:>5.2f} Sharpe={m.sharpe:>5.2f} "
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f"Ret={m.total_return*100:>+7.2f}%")
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# ── ranked table ──────────────────────────────────────────────────────────
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results.sort(key=lambda x: x[2].sharpe, reverse=True)
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profitable = [r for r in results if r[2].total_return > 0]
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losing = [r for r in results if r[2].total_return <= 0]
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max_ret = max(abs(r[2].total_return) for r in results) if results else 1
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print()
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print("─" * 72)
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print(f" {'Symbol':<10} {'Bars':>6} {'Trades':>6} {'WR%':>5} {'PF':>5} "
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f"{'Sharpe':>6} {'Return%':>8} {'MDD%':>6} Return bar")
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print("─" * 72)
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for symbol, n_candles, m, _ in results:
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if m.n_trades == 0:
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continue
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b = bar(m.total_return, max_ret)
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print(f" {symbol:<10} {n_candles:>6} {m.n_trades:>6} {m.win_rate*100:>5.1f} "
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f"{m.profit_factor:>5.2f} {m.sharpe:>6.2f} {m.total_return*100:>+8.2f} "
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f"{m.max_drawdown*100:>6.2f} {b}")
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print("─" * 72)
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print(f" Profitable: {len(profitable)}/{len(results)} pairs "
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f"({len(profitable)/len(results)*100:.0f}%)")
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# ── portfolio equity (equal-weight, 1 trade at a time per symbol) ─────────
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# Normalise each equity curve to return-fraction, then average
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if results:
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min_len = min(len(eq) for _, _, _, eq in results)
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normed = [eq[:min_len] / eq[0] for _, _, _, eq in results]
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port_equity = np.mean(normed, axis=0) * INITIAL_BALANCE
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total_ret = port_equity[-1] / port_equity[0] - 1
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peak = np.maximum.accumulate(port_equity)
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port_mdd = ((port_equity - peak) / peak).min()
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print()
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print("── Portfolio (equal-weight average) ─────────────────────────────")
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print(f" Return : {total_ret*100:+.2f}%")
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print(f" Max DD : {port_mdd*100:.2f}%")
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# ── recommended live symbols ───────────────────────────────────────────────
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live_candidates = [r for r in results
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if r[2].sharpe > 1.5 and r[2].n_trades >= 15
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and r[2].profit_factor > 1.2 and r[2].total_return > 0]
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print()
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print("── Recommended symbols for live trading ─────────────────────────")
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if live_candidates:
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for symbol, _, m, _ in live_candidates[:6]:
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print(f" ✓ {symbol:<10} Sharpe={m.sharpe:.2f} "
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f"PF={m.profit_factor:.2f} Return={m.total_return*100:+.1f}%")
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else:
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print(" (None met Sharpe>1.5 + PF>1.2 + ≥15 trades)")
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print()
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print("=" * 72)
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if __name__ == "__main__":
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main()
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