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#!/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 = 90, base_price: float = 150.0) -> list[Candle]:
"""Generate realistic SOL/USDT 5-minute candles.
Simulates:
- Mild uptrend with periodic sharp dips during Asian session
- Intraday volatility (higher at session opens)
- Random walk with mean reversion
- Occasional momentum bursts that create RSI extremes
"""
random.seed(42)
candles = []
price = base_price
start = datetime(2025, 1, 1, tzinfo=timezone.utc)
for day in range(days):
# Mild upward bias to keep price above EMA
daily_trend = random.uniform(-0.005, 0.015)
# Many days have a sharp V-dip during Asian session (1-2 bar crash + recovery)
# This creates RSI oversold while EMA stays above price briefly
dip_day = random.random() < 0.45
dip_bar = random.randint(4, 18) if dip_day else -1
# Sharp single-bar dip: 2-4% drop then immediate recovery
dip_magnitude = 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: # Asian open (our trading window)
vol = 0.003
elif 13 <= hour < 16: # US session
vol = 0.0025
else:
vol = 0.0015
# Base random walk with upward drift
change = random.gauss(daily_trend / 288, vol)
mean_rev = (base_price - price) / base_price * 0.001
change += mean_rev
# Session bar index within 00:00-01:55 UTC (bars 0-23)
session_bar = bar
# Inject sharp V-dip: 1 bar crash, 1 bar partial recovery
if dip_day and 0 <= hour < 2:
if session_bar == dip_bar:
# Crash bar: sharp drop
change = -dip_magnitude
elif session_bar == dip_bar + 1:
# Recovery bar: bounce back most of the way
change = dip_magnitude * random.uniform(0.5, 0.8)
elif session_bar == dip_bar + 2:
# Continued recovery
change = dip_magnitude * random.uniform(0.1, 0.3)
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 *= 2.5 # Spike 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, params, balance=750.0, slippage=0.001, fee=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():
print("=" * 60)
print("Asian Session RSI — Parameter Optimization")
print("SOL/USDT 5m | Capital: $750 (~100만원)")
print("=" * 60)
days = 30
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 = []
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 = []
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']}, 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()
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