ADD: added optimizer workflow

This commit is contained in:
2026-01-23 18:22:56 +01:00
parent da68106666
commit fc54074a59
14 changed files with 503 additions and 178 deletions

View File

@@ -1,4 +1,6 @@
import os
import csv
import argparse
import pandas as pd
import pandas_ta as ta
from dotenv import load_dotenv
@@ -18,6 +20,10 @@ load_dotenv()
API_KEY = os.getenv('ALPACA_API_KEY')
SECRET_KEY = os.getenv('ALPACA_SECRET_KEY')
PATH_TO_OUTPUT = "output/"
# Ensure output folder exists
os.makedirs(PATH_TO_OUTPUT, exist_ok=True)
# --- KLASSE: DATEN-ENGINE ---
class DataEngine:
@staticmethod
@@ -27,7 +33,7 @@ class DataEngine:
request = StockBarsRequest(
symbol_or_symbols=[symbol],
timeframe=TimeFrame.Day,
timeframe=TimeFrame.Hour,
start=start_date
)
@@ -43,18 +49,52 @@ class DataEngine:
# --- KLASSE: STRATEGIE (RSI) ---
class MyRsiStrategy(Strategy):
# Parameter - diese können später optimiert werden
rsi_period = 25
rsi_low = 30
rsi_high = 70
rsi_period = 29
rsi_low = 35
rsi_high = 75
# Position sizing: fraction of equity to allocate per new trade (0-1)
position_size_pct = 0.1
# Fallback fixed minimum shares to buy if computed shares is 0
min_shares = 1
# ATR-based stop-loss parameters
atr_period = 18
# Stop-loss multiplier applied to ATR (e.g. 3 -> stop = entry - 3 * ATR)
stop_loss_atr_multiplier = 4.0
def init(self):
# Indikator berechnen (self.I stellt sicher, dass er im Chart erscheint)
self.rsi = self.I(ta.rsi, pd.Series(self.data.Close), length=self.rsi_period)
# Average True Range for volatility-based stops
self.atr = self.I(
ta.atr,
pd.Series(self.data.High),
pd.Series(self.data.Low),
pd.Series(self.data.Close),
length=self.atr_period,
)
def next(self):
# KAUFEN: Wenn RSI die untere Grenze von unten nach oben kreuzt
if crossover(self.rsi, self.rsi_low):
self.buy()
price = float(self.data.Close[-1])
# Use a conservative fraction of current equity to size the position
max_invest = max(0, self.equity * self.position_size_pct)
shares = int(max_invest // price)
if shares < self.min_shares:
shares = self.min_shares
# Compute ATR-based stop-loss price below entry
atr_value = float(self.atr[-1]) if not pd.isna(self.atr[-1]) else None
if atr_value and atr_value > 0:
stop_price = round(price - (self.stop_loss_atr_multiplier * atr_value), 6)
# Ensure stop is below price
if stop_price >= price:
stop_price = round(price * 0.99, 6)
else:
# Fallback to a small percent-based stop if ATR not ready
stop_price = round(price * 0.98, 6)
# Place an integer-sized buy order with SL (avoids relative-size cancellation)
self.buy(size=shares, sl=stop_price)
# VERKAUFEN: Wenn RSI die obere Grenze von oben nach unten kreuzt
elif crossover(self.rsi_high, self.rsi):
@@ -62,18 +102,17 @@ class MyRsiStrategy(Strategy):
self.position.close()
# --- HAUPTPROGRAMM ---
def run_backtest(symbol="AAPL", cash=10000, commission=0.002):
def run_backtest(symbol="AAPL", days=365, cash=1000, commission=0.002):
# 1. Daten laden
print(f"Lade Daten für {symbol}...")
data = DataEngine.get_alpaca_data(symbol)
data = DataEngine.get_alpaca_data(symbol, days=days)
# 2. Backtest initialisieren
# commission=0.002 bedeutet 0.2% Gebühren pro Trade
bt = Backtest(data, MyRsiStrategy, cash=cash, commission=commission)
bt = Backtest(data, MyRsiStrategy, cash=cash, commission=commission, finalize_trades=True)
# 3. Backtest ausführen
stats = bt.run()
# --- AUSWERTUNG ---
print("\n" + "="*30)
print(f"BACKTEST ERGEBNISSE FÜR {symbol}")
@@ -86,20 +125,85 @@ def run_backtest(symbol="AAPL", cash=10000, commission=0.002):
print(f"Win Rate [%]: {stats['Win Rate [%]']:.2f}%")
print("="*30)
result = {
'symbol': symbol,
'start_cash': cash,
'end_equity': stats.get('Equity Final [$]', None),
'return_pct': stats.get('Return [%]', None),
'max_drawdown_pct': stats.get('Max. Drawdown [%]', None),
'n_trades': stats.get('# Trades', None),
'win_rate_pct': stats.get('Win Rate [%]', None),
}
# 4. EXPORT: Trades nach Excel
trades = stats['_trades']
if not trades.empty:
# Dauer der Trades berechnen
trades = stats.get('_trades')
if trades is not None and not trades.empty:
trades['Duration'] = trades['ExitTime'] - trades['EntryTime']
excel_name = f"Backtest_Trades_{symbol}.xlsx"
excel_name = os.path.join(PATH_TO_OUTPUT, f"Backtest_Trades_{symbol}.xlsx")
trades.to_excel(excel_name)
print(f"✅ Excel-Liste gespeichert: {excel_name}")
result['trades_file'] = excel_name
# 5. EXPORT: Interaktiver HTML Report
report_name = f"Backtest_Report_{symbol}.html"
report_name = os.path.join(PATH_TO_OUTPUT, f"Backtest_Report_{symbol}.html")
bt.plot(filename=report_name, open_browser=False)
print(f"✅ Interaktiver Chart gespeichert: {report_name}")
result['report_file'] = report_name
return result
def run_backtests(symbols, days=365, cash=1000, commission=0.002, summary_name=None):
summary = []
for sym in symbols:
try:
res = run_backtest(symbol=sym, days=days, cash=cash, commission=commission)
summary.append(res)
except Exception as e:
print(f"⚠️ Fehler beim Backtest für {sym}: {e}")
summary.append({'symbol': sym, 'error': str(e)})
# Write summary CSV
if summary_name is None:
ts = datetime.now().strftime('%Y%m%d_%H%M%S')
summary_name = os.path.join(PATH_TO_OUTPUT, f"backtest_summary_{ts}.csv")
keys = set()
for row in summary:
keys.update(row.keys())
keys = list(keys)
with open(summary_name, 'w', newline='', encoding='utf-8') as csvfile:
writer = csv.DictWriter(csvfile, fieldnames=keys)
writer.writeheader()
for row in summary:
writer.writerow(row)
print(f"\n✅ Zusammenfassung gespeichert: {summary_name}")
return summary_name
if __name__ == "__main__":
# Starte den Backtest
run_backtest(symbol="AAPL", cash=10000)
parser = argparse.ArgumentParser(description='Batch backtester for multiple stocks')
parser.add_argument('--tickers', type=str, help='Comma-separated list of tickers, e.g. AAPL,MSFT,TSLA')
parser.add_argument('--file', type=str, help='Path to a file containing one ticker per line')
parser.add_argument('--days', type=int, default=365, help='Days of historical data to fetch')
parser.add_argument('--cash', type=float, default=1000, help='Starting cash per backtest')
parser.add_argument('--commission', type=float, default=0.002, help='Commission (fraction), e.g. 0.002')
args = parser.parse_args()
symbols = []
if args.tickers:
symbols = [s.strip().upper() for s in args.tickers.split(',') if s.strip()]
elif args.file:
if os.path.exists(args.file):
with open(args.file, 'r', encoding='utf-8') as f:
symbols = [line.strip().upper() for line in f if line.strip()]
else:
print(f"Ticker-Datei nicht gefunden: {args.file}")
raise SystemExit(1)
else:
symbols = ["AAPL"]
print(f"Starte Batch-Backtest für: {symbols}")
summary_csv = run_backtests(symbols, days=args.days, cash=args.cash, commission=args.commission)
print(f"Fertig. Übersicht: {summary_csv}")