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import pandas as pd
import pandas_ta_classic as ta
import yfinance as yf
import numpy as np
import logging
import json
from typing import Dict, Any, List, Tuple, Optional
from pycoingecko import CoinGeckoAPI
logger = logging.getLogger(__name__)
cg = CoinGeckoAPI()
# Common crypto symbol mappings (can be extended)
COMMON_CRYPTO_SYMBOLS = {'BTC', 'ETH', 'SHIB', 'SOL', 'XRP', 'ADA', 'DOGE', 'USDC', 'USDT'}
def _get_coingecko_id(symbol: str) -> Optional[str]:
"""
Dynamically search for CoinGecko ID using the search API.
Falls back to common mappings for performance.
"""
symbol = symbol.upper()
# Try common mappings first (fast)
common_map = {
'BTC': 'bitcoin',
'ETH': 'ethereum',
'SHIB': 'shiba-inu',
'SOL': 'solana',
'XRP': 'ripple',
'ADA': 'cardano',
'DOGE': 'dogecoin',
'USDC': 'usd-coin',
'USDT': 'tether',
}
if symbol in common_map:
return common_map[symbol]
# Dynamic search for other symbols
try:
results = cg.search(query=symbol)
if results.get('coins'):
return results['coins'][0]['id']
except Exception as e:
logger.debug(f"CoinGecko search failed for {symbol}: {e}")
return None
def _fetch_crypto_data(coin_id: str, start: str, end: str):
"""Fetch historical crypto data from CoinGecko."""
try:
data = cg.get_coin_market_chart_range_by_id(
id=coin_id,
vs_currency='usd',
from_timestamp=int(pd.to_datetime(start).timestamp()),
to_timestamp=int(pd.to_datetime(end).timestamp())
)
prices = data['prices']
volumes = data['total_volumes']
df = pd.DataFrame({
'Date': pd.to_datetime([p[0] for p in prices], unit='ms'),
'Close': [p[1] for p in prices],
'Volume': [v[1] for v in volumes] if volumes else [0] * len(prices)
})
df.set_index('Date', inplace=True)
return df
except Exception as e:
logger.error(f"Failed to fetch crypto data for {coin_id}: {e}")
return pd.DataFrame()
def _fetch_data(symbol: str, start: str, end: str):
"""Fetch data from either yfinance (stocks) or CoinGecko (crypto)."""
# Try to find CoinGecko ID
coin_id = _get_coingecko_id(symbol)
if coin_id:
logger.info(f"Recognized {symbol} as crypto (CoinGecko ID: {coin_id})")
return _fetch_crypto_data(coin_id, start, end)
else:
# Fall back to stock data
logger.info(f"Treating {symbol} as stock symbol")
return yf.download(symbol, start=start, end=end, progress=False)
def run_backtest(symbol: str, fast_ma: int, slow_ma: int, start_date: str = "2020-01-01", end_date: str = "2023-12-31", visualize: bool = False) -> str:
"""Backtest MA Crossover strategy. Example: run_backtest("AAPL", 10, 50, "2023-01-01", "2023-12-31", visualize=True)"""
try:
logger.info(f"Starting backtest for {symbol} (Fast: {fast_ma}, Slow: {slow_ma}) from {start_date} to {end_date}")
# Use yfinance directly instead of get_price
df = _fetch_data(symbol, start_date, end_date)
if df.empty:
logger.warning(f"Backtest failed: No data for {symbol}")
return f"No data found for {symbol}"
# Strategy Logic
df['Fast_MA'] = ta.sma(df['Close'], length=fast_ma)
df['Slow_MA'] = ta.sma(df['Close'], length=slow_ma)
# Signals
df['Signal'] = 0
df.loc[df['Fast_MA'] > df['Slow_MA'], 'Signal'] = 1
df['Position'] = df['Signal'].diff()
# Calculate Returns
df['Market_Return'] = df['Close'].pct_change()
df['Strategy_Return'] = df['Market_Return'] * df['Signal'].shift(1)
# Equity Curves
df['Strategy_Equity'] = (1 + df['Strategy_Return']).cumprod()
df['BuyHold_Equity'] = (1 + df['Market_Return']).cumprod()
# Metrics
total_return = (1 + df['Strategy_Return']).prod() - 1
buy_hold_return = (1 + df['Market_Return']).prod() - 1
# Max Drawdown
cum_ret = (1 + df['Strategy_Return']).cumprod()
peak = cum_ret.expanding(min_periods=1).max()
dd = (cum_ret / peak) - 1
max_dd = dd.min()
# Sharpe Ratio (assuming 0% risk free for simplicity or use config)
sharpe = df['Strategy_Return'].mean() / df['Strategy_Return'].std() * np.sqrt(252)
result = (
f"Backtest Results for {symbol} ({start_date} to {end_date}):\n"
f"Strategy: MA Crossover ({fast_ma}/{slow_ma})\n"
f"------------------------------------------------\n"
f"Total Return: {total_return:.2%}\n"
f"Buy & Hold Return: {buy_hold_return:.2%}\n"
f"Sharpe Ratio: {sharpe:.2f}\n"
f"Max Drawdown: {max_dd:.2%}"
)
if visualize:
try:
from tools.visualizer import plot_line
df_clean = df.dropna(subset=['Strategy_Equity', 'BuyHold_Equity'])
chart = plot_line(
{
'x': list(range(len(df_clean))),
'y': [df_clean['Strategy_Equity'].tolist(), df_clean['BuyHold_Equity'].tolist()]
},
x_label="Days",
y_label="Equity (Initial = $1)",
title=f"Backtest: {symbol} MA({fast_ma}/{slow_ma})",
labels=["Strategy", "Buy & Hold"]
)
result += f"\n\n{chart}"
except Exception as e:
logger.error(f"Error generating backtest chart: {e}")
result += f"\n(Visualization error: {str(e)})"
logger.info(f"Backtest completed for {symbol}. Return: {total_return:.2%}")
return result
except Exception as e:
logger.error(f"Backtest failed for {symbol}: {e}", exc_info=True)
return f"Error running backtest: {str(e)}"
def walk_forward_analysis(symbol: str, start_date: str = "2020-01-01", end_date: str = "2023-12-31", train_months: int = 12, test_months: int = 3) -> str:
"""Optimize strategy parameters using Walk Forward Analysis. Example: walk_forward_analysis("AAPL", "2022-01-01", "2023-12-31", train_months=6, test_months=3)"""
df = _fetch_data(symbol, start_date, end_date)
if df.empty:
return "No data found."
close = df['Close']
if isinstance(close, pd.DataFrame):
close = close.iloc[:, 0]
# Generate windows
# Simplified: Iterate by index assuming daily data
# 21 days/month
train_len = train_months * 21
test_len = test_months * 21
step = test_len
results = []
# Parameter Grid
fast_params = [10, 20, 50]
slow_params = [50, 100, 200]
current_idx = 0
while current_idx + train_len + test_len < len(df):
train_data = close.iloc[current_idx : current_idx + train_len]
test_data = close.iloc[current_idx + train_len : current_idx + train_len + test_len]
# Optimize on Train
best_perf = -np.inf
best_params = (0, 0)
for f in fast_params:
for s in slow_params:
if f >= s: continue
# Vectorized backtest on train
fast_ma = train_data.rolling(window=f).mean()
slow_ma = train_data.rolling(window=s).mean()
signal = (fast_ma > slow_ma).astype(int).shift(1)
ret = train_data.pct_change() * signal
perf = ret.sum() # Simple sum of returns
if perf > best_perf:
best_perf = perf
best_params = (f, s)
# Test on Test Data
f, s = best_params
fast_ma_test = test_data.rolling(window=f).mean()
slow_ma_test = test_data.rolling(window=s).mean()
signal_test = (fast_ma_test > slow_ma_test).astype(int).shift(1)
ret_test = test_data.pct_change() * signal_test
test_perf = ret_test.sum()
results.append({
"Period": f"{test_data.index[0].date()} to {test_data.index[-1].date()}",
"Best Params": best_params,
"Test Return": test_perf
})
current_idx += step
# Format Output
output = ["Walk Forward Analysis Results:"]
total_ret = 0
for r in results:
output.append(f"[{r['Period']}] Params: {r['Best Params']}, Return: {r['Test Return']:.2%}")
total_ret += r['Test Return']
output.append(f"Total Walk Forward Return: {total_ret:.2%}")
return "\n".join(output)
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