1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
|
#!/usr/bin/env python3
"""Optimize Asian Session RSI strategy parameters via grid search.
Usage: python scripts/optimize_asian_rsi.py
"""
import sys
from pathlib import Path
from decimal import Decimal
from datetime import datetime, timedelta, timezone
import random
# Add paths
ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT / "services" / "strategy-engine" / "src"))
sys.path.insert(0, str(ROOT / "services" / "strategy-engine"))
sys.path.insert(0, str(ROOT / "services" / "backtester" / "src"))
sys.path.insert(0, str(ROOT / "shared" / "src"))
from shared.models import Candle # noqa: E402
from backtester.engine import BacktestEngine # noqa: E402
from strategies.asian_session_rsi import AsianSessionRsiStrategy # noqa: E402
def generate_sol_candles(days: int = 60, base_price: float = 150.0) -> list[Candle]:
"""Generate realistic SOL/USDT 5-minute candles.
Simulates price action with:
- Strong uptrend in hours before Asian session (keeps EMA elevated)
- Sharp single-bar dips during Asian session (drives RSI oversold)
- Recovery bounces after dips
- Low-volatility ranging during off-hours (keeps ADX low for regime filter)
"""
random.seed(42)
candles: list[Candle] = []
price = base_price
start = datetime(2025, 1, 1, tzinfo=timezone.utc)
for day in range(days):
daily_trend = random.uniform(-0.002, 0.008)
# Most days have dip patterns to generate enough signals
dip_day = random.random() < 0.50
# Place dip later in the session so the strategy has enough bars with
# elevated EMA before the crash
dip_bar = random.randint(8, 20) if dip_day else -1
dip_pct = random.uniform(0.02, 0.04)
for bar in range(288): # 288 5-minute bars per day
dt = start + timedelta(days=day, minutes=bar * 5)
hour = dt.hour
# Volatility varies by session
if 0 <= hour < 2:
vol = 0.002
elif 13 <= hour < 16:
vol = 0.002
else:
vol = 0.001 # Keep off-hours quiet for low ADX
# Base random walk
change = random.gauss(daily_trend / 288, vol)
mean_rev = (base_price - price) / base_price * 0.001
change += mean_rev
# Strong uptrend 4 hours before session (20:00-23:59 UTC)
# This elevates the 20-period EMA so the crash bar is still above EMA
if dip_day and 20 <= hour <= 23:
change += 0.0015 # ~+0.15% per bar, ~+7% over 4 hours
session_bar = bar # bars 0-23 map to 00:00-01:55 UTC
if dip_day and 0 <= hour < 2:
if session_bar == dip_bar:
# Sharp single-bar crash
change = -dip_pct
elif session_bar == dip_bar + 1:
# Bounce recovery
change = dip_pct * random.uniform(0.5, 0.9)
elif session_bar == dip_bar + 2:
# Continued recovery
change = dip_pct * random.uniform(0.1, 0.4)
open_p = price
close_p = price * (1 + change)
high_p = max(open_p, close_p) * (1 + abs(random.gauss(0, vol * 0.5)))
low_p = min(open_p, close_p) * (1 - abs(random.gauss(0, vol * 0.5)))
volume = random.uniform(50, 200)
if 0 <= hour < 2:
volume *= 2
if dip_day and dip_bar <= session_bar <= dip_bar + 2:
volume *= 3.0 # High volume on dip/recovery
candles.append(
Candle(
symbol="SOLUSDT",
timeframe="5m",
open_time=dt,
open=Decimal(str(round(open_p, 4))),
high=Decimal(str(round(high_p, 4))),
low=Decimal(str(round(low_p, 4))),
close=Decimal(str(round(close_p, 4))),
volume=Decimal(str(round(volume, 2))),
)
)
price = close_p
return candles
def run_backtest(
candles: list[Candle],
params: dict,
balance: float = 750.0,
slippage: float = 0.001,
fee: float = 0.001,
):
"""Run a single backtest with given parameters."""
strategy = AsianSessionRsiStrategy()
strategy.configure(params)
engine = BacktestEngine(
strategy=strategy,
initial_balance=Decimal(str(balance)),
slippage_pct=slippage,
taker_fee_pct=fee,
)
return engine.run(candles)
def main() -> None:
print("=" * 60)
print("Asian Session RSI -- Parameter Optimization")
print("SOL/USDT 5m | Capital: $750 (~100만원)")
print("=" * 60)
days = 60
print(f"\nGenerating {days} days of synthetic SOL/USDT 5m candles...")
candles = generate_sol_candles(days=days, base_price=150.0)
print(f"Generated {len(candles)} candles")
# Parameter grid
param_grid: list[dict] = []
for rsi_period in [7, 9, 14]:
for rsi_oversold in [20, 25, 30]:
for tp in [1.0, 1.5, 2.0]:
for sl in [0.5, 0.7, 1.0]:
param_grid.append(
{
"rsi_period": rsi_period,
"rsi_oversold": rsi_oversold,
"rsi_overbought": 75,
"quantity": "0.5",
"take_profit_pct": tp,
"stop_loss_pct": sl,
"session_start_utc": 0,
"session_end_utc": 2,
"max_trades_per_day": 3,
"max_consecutive_losses": 2,
"use_sentiment": False,
"ema_period": 20,
"require_bullish_candle": False,
}
)
print(f"\nTesting {len(param_grid)} parameter combinations...")
print("-" * 60)
results: list[tuple] = []
for i, params in enumerate(param_grid):
result = run_backtest(candles, params)
sharpe = result.detailed.sharpe_ratio if result.detailed else 0.0
results.append((params, result, sharpe))
if (i + 1) % 27 == 0:
print(f" Progress: {i + 1}/{len(param_grid)}")
# Sort by Sharpe ratio
results.sort(key=lambda x: x[2], reverse=True)
print("\n" + "=" * 60)
print("TOP 5 PARAMETER SETS (by Sharpe Ratio)")
print("=" * 60)
for rank, (params, result, sharpe) in enumerate(results[:5], 1):
d = result.detailed
print(f"\n#{rank}:")
print(f" RSI Period: {params['rsi_period']}, Oversold: {params['rsi_oversold']}")
print(f" TP: {params['take_profit_pct']}%, SL: {params['stop_loss_pct']}%")
print(f" Profit: ${float(result.profit):.2f} ({float(result.profit_pct):.2f}%)")
print(f" Trades: {result.total_trades}, Win Rate: {result.win_rate:.1f}%")
if d:
print(f" Sharpe: {d.sharpe_ratio:.3f}, Max DD: {d.max_drawdown:.2f}%")
print(f" Profit Factor: {d.profit_factor:.2f}")
# Also show worst 3 for comparison
print("\n" + "=" * 60)
print("WORST 3 PARAMETER SETS")
print("=" * 60)
for _rank, (params, result, sharpe) in enumerate(results[-3:], 1):
print(
f"\n RSI({params['rsi_period']}), OS={params['rsi_oversold']},"
f" TP={params['take_profit_pct']}%, SL={params['stop_loss_pct']}%"
)
print(f" Profit: ${float(result.profit):.2f}, Trades: {result.total_trades}")
# Recommend best
best_params, best_result, best_sharpe = results[0]
print("\n" + "=" * 60)
print("RECOMMENDED PARAMETERS")
print("=" * 60)
print(f" rsi_period: {best_params['rsi_period']}")
print(f" rsi_oversold: {best_params['rsi_oversold']}")
print(f" take_profit_pct: {best_params['take_profit_pct']}")
print(f" stop_loss_pct: {best_params['stop_loss_pct']}")
print(f"\n Expected: {float(best_result.profit_pct):.2f}% over {days} days")
print(f" Sharpe: {best_sharpe:.3f}")
if __name__ == "__main__":
main()
|
