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feat: add symbol comparison and position sizing analysis tools

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- Add payoff_ratio and max_consecutive_losses to backtester summary
- Add compare_symbols.py: per-symbol parameter sweep for candidate evaluation
- Add position_sizing_analysis.py: robust Monte Carlo position sizing
- Fetch historical data for SOL, LINK, AVAX candidates (365 days)
- Update existing symbol data (XRP, TRX, DOGE)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
21in7
2026-03-18 23:35:42 +09:00
parent 9d9f4960fc
commit 5b3f6af13c
10 changed files with 649 additions and 4 deletions
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43
src/backtester.py
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@@ -562,6 +562,21 @@ class Backtester:
else:
sharpe = 0.0
# 손익비 (avg_win / |avg_loss|)
avg_w = float(np.mean(wins)) if wins else 0.0
avg_l = float(np.mean(losses)) if losses else 0.0
payoff_ratio = round(avg_w / abs(avg_l), 2) if avg_l != 0 else float("inf")
# 최대 연속 손실 횟수
max_consec_loss = 0
cur_streak = 0
for p in pnls:
if p <= 0:
cur_streak += 1
max_consec_loss = max(max_consec_loss, cur_streak)
else:
cur_streak = 0
# 청산 사유별 비율
reasons = {}
for t in self.trades:
@@ -573,8 +588,10 @@ class Backtester:
"total_pnl": round(total_pnl, 4),
"return_pct": round(total_pnl / self.cfg.initial_balance * 100, 2),
"win_rate": round(len(wins) / len(self.trades) * 100, 2) if self.trades else 0.0,
"avg_win": round(np.mean(wins), 4) if wins else 0.0,
"avg_loss": round(np.mean(losses), 4) if losses else 0.0,
"avg_win": round(avg_w, 4),
"avg_loss": round(avg_l, 4),
"payoff_ratio": payoff_ratio,
"max_consecutive_losses": max_consec_loss,
"profit_factor": round(gross_profit / gross_loss, 2) if gross_loss > 0 else float("inf"),
"max_drawdown_pct": round(mdd, 2),
"sharpe_ratio": round(sharpe, 2),
@@ -797,6 +814,7 @@ class WalkForwardBacktester:
if not all_trades:
summary = {"total_trades": 0, "total_pnl": 0.0, "return_pct": 0.0,
"win_rate": 0.0, "avg_win": 0.0, "avg_loss": 0.0,
"payoff_ratio": 0.0, "max_consecutive_losses": 0,
"profit_factor": 0.0, "max_drawdown_pct": 0.0,
"sharpe_ratio": 0.0, "total_fees": 0.0, "close_reasons": {}}
else:
@@ -820,6 +838,21 @@ class WalkForwardBacktester:
else:
sharpe = 0.0
# 손익비 (avg_win / |avg_loss|)
avg_w = float(np.mean(wins)) if wins else 0.0
avg_l = float(np.mean(losses)) if losses else 0.0
payoff_ratio = round(avg_w / abs(avg_l), 2) if avg_l != 0 else float("inf")
# 최대 연속 손실 횟수
max_consec_loss = 0
cur_streak = 0
for p in pnls:
if p <= 0:
cur_streak += 1
max_consec_loss = max(max_consec_loss, cur_streak)
else:
cur_streak = 0
reasons = {}
for t in all_trades:
r = t["close_reason"]
@@ -830,8 +863,10 @@ class WalkForwardBacktester:
"total_pnl": round(total_pnl, 4),
"return_pct": round(total_pnl / self.cfg.initial_balance * 100, 2),
"win_rate": round(len(wins) / len(all_trades) * 100, 2),
"avg_win": round(np.mean(wins), 4) if wins else 0.0,
"avg_loss": round(np.mean(losses), 4) if losses else 0.0,
"avg_win": round(avg_w, 4),
"avg_loss": round(avg_l, 4),
"payoff_ratio": payoff_ratio,
"max_consecutive_losses": max_consec_loss,
"profit_factor": round(gross_profit / gross_loss, 2) if gross_loss > 0 else float("inf"),
"max_drawdown_pct": round(mdd, 2),
"sharpe_ratio": round(sharpe, 2),