318 lines
9.9 KiB
Python
318 lines
9.9 KiB
Python
import sys
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from typing import Any, Dict, List, Optional
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import pandas as pd
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import numpy as np
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# ============= statsmodels ===================
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from statsmodels.tsa.vector_ar.vecm import VECM
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from backtest_configs import CRYPTO_CONFIG
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from tools.data_loader import load_market_data, transform_dataframe
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from tools.trading_pair import TradingPair
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from results import BacktestResult
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NanoPerMin = 1e9
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UNSET_FLOAT: float = sys.float_info.max
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UNSET_INT: int = sys.maxsize
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# # ==========================================================================
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CONFIG = CRYPTO_CONFIG
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# CONFIG = EQT_CONFIG
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BacktestResults = BacktestResult(config=CONFIG)
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def create_trading_signals(pair: TradingPair) -> pd.DataFrame:
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result_columns = [
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"time",
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"action",
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"symbol",
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"price",
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"disequilibrium",
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"scaled_disequilibrium",
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"pair",
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]
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testing_pair_df = pair.testing_df_
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next_values = pair.vecm_fit_.predict(steps=len(testing_pair_df))
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colname_a, colname_b = pair.colnames()
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# Convert prediction to a DataFrame for readability
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predicted_df = pd.DataFrame(next_values, columns=[colname_a, colname_b])
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beta = pair.vecm_fit_.beta
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pair_result_df = pd.merge(
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testing_pair_df.reset_index(drop=True),
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predicted_df,
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left_index=True,
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right_index=True,
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suffixes=("", "_pred"),
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).dropna()
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pair_result_df["disequilibrium"] = pair_result_df[pair.colnames()] @ beta
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pair_result_df["scaled_disequilibrium"] = abs(
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pair_result_df["disequilibrium"] - pair.training_mu_
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) / pair.training_std_
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# Reset index to ensure proper indexing
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pair_result_df = pair_result_df.reset_index()
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# Iterate through the testing dataset to find the first trading opportunity
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open_row_index = None
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initial_abs_term = None
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open_threshold = CONFIG["dis-equilibrium_open_trshld"]
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close_threshold = CONFIG["dis-equilibrium_close_trshld"]
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for row_idx in range(len(pair_result_df)):
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curr_disequilibrium = pair_result_df["scaled_disequilibrium"][row_idx]
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# Check if current row has sufficient disequilibrium (not near-zero)
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if curr_disequilibrium >= open_threshold:
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open_row_index = row_idx
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initial_abs_term = curr_disequilibrium
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break
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# If no row with sufficient disequilibrium found, skip this pair
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if open_row_index is None:
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print(f"{pair}: Insufficient disequilibrium in testing dataset. Skipping.")
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return pd.DataFrame()
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# Look for close signal starting from the open position
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trading_signals_df = (pair_result_df["scaled_disequilibrium"][open_row_index:] < close_threshold)
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# Adjust indices to account for the offset from open_row_index
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close_row_index = None
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for idx, value in trading_signals_df.items():
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if value:
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close_row_index = idx
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break
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open_row = pair_result_df.loc[open_row_index]
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open_tstamp = open_row["tstamp"]
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open_disequilibrium = open_row["disequilibrium"]
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open_scaled_disequilibrium = open_row["scaled_disequilibrium"]
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open_px_a = open_row[f"{colname_a}"]
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open_px_b = open_row[f"{colname_b}"]
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abs_beta = abs(beta[1])
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pred_px_b = pair_result_df.loc[open_row_index][f"{colname_b}_pred"]
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pred_px_a = pair_result_df.loc[open_row_index][f"{colname_a}_pred"]
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if pred_px_b * abs_beta - pred_px_a > 0:
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open_side_a = "BUY"
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open_side_b = "SELL"
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close_side_a = "SELL"
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close_side_b = "BUY"
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else:
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open_side_b = "BUY"
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open_side_a = "SELL"
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close_side_b = "SELL"
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close_side_a = "BUY"
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# If no close signal found, print position and unrealized PnL
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if close_row_index is None:
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last_row_index = len(pair_result_df) - 1
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# Use the new method from BacktestResult to handle outstanding positions
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BacktestResults.handle_outstanding_position(
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pair=pair,
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pair_result_df=pair_result_df,
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last_row_index=last_row_index,
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open_side_a=open_side_a,
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open_side_b=open_side_b,
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open_px_a=open_px_a,
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open_px_b=open_px_b,
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open_tstamp=open_tstamp,
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initial_abs_term=initial_abs_term,
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colname_a=colname_a,
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colname_b=colname_b
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)
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# Return only open trades (no close trades)
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trd_signal_tuples = [
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(
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open_tstamp,
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open_side_a,
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pair.symbol_a_,
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open_px_a,
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open_disequilibrium,
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open_scaled_disequilibrium,
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pair,
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),
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(
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open_tstamp,
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open_side_b,
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pair.symbol_b_,
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open_px_b,
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open_disequilibrium,
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open_scaled_disequilibrium,
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pair,
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),
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]
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else:
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# Close signal found - create complete trade
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close_row = pair_result_df.loc[close_row_index]
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close_tstamp = close_row["tstamp"]
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close_disequilibrium = close_row["disequilibrium"]
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close_scaled_disequilibrium = close_row["scaled_disequilibrium"]
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close_px_a = close_row[f"{colname_a}"]
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close_px_b = close_row[f"{colname_b}"]
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print(f"{pair}: Close signal found at index {close_row_index}")
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trd_signal_tuples = [
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(
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open_tstamp,
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open_side_a,
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pair.symbol_a_,
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open_px_a,
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open_disequilibrium,
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open_scaled_disequilibrium,
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pair,
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),
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(
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open_tstamp,
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open_side_b,
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pair.symbol_b_,
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open_px_b,
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open_disequilibrium,
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open_scaled_disequilibrium,
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pair,
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),
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(
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close_tstamp,
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close_side_a,
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pair.symbol_a_,
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close_px_a,
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close_disequilibrium,
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close_scaled_disequilibrium,
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pair,
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),
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(
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close_tstamp,
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close_side_b,
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pair.symbol_b_,
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close_px_b,
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close_disequilibrium,
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close_scaled_disequilibrium,
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pair,
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),
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]
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# Add tuples to data frame
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return pd.DataFrame(
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trd_signal_tuples,
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columns=result_columns,
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)
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def run_single_pair(
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pair: TradingPair, market_data: pd.DataFrame, price_column: str
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) -> Optional[pd.DataFrame]:
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pair.get_datasets(
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market_data=market_data, training_minutes=CONFIG["training_minutes"]
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)
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try:
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is_cointegrated = pair.train_pair()
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if not is_cointegrated:
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print(f"{pair} IS NOT COINTEGRATED")
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return None
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except Exception as e:
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print(f"{pair}: Training failed: {str(e)}")
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return None
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try:
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pair_trades = create_trading_signals(
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pair=pair,
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)
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except Exception as e:
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print(f"{pair}: Prediction failed: {str(e)}")
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return None
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return pair_trades
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def run_pairs(config: Dict, market_data_df: pd.DataFrame, price_column: str) -> None:
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def _create_pairs(config: Dict) -> List[TradingPair]:
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instruments = config["instruments"]
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all_indexes = range(len(instruments))
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unique_index_pairs = [(i, j) for i in all_indexes for j in all_indexes if i < j]
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pairs = []
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for a_index, b_index in unique_index_pairs:
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symbol_a = instruments[a_index]
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symbol_b = instruments[b_index]
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pair = TradingPair(symbol_a, symbol_b, price_column)
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pairs.append(pair)
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return pairs
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pairs_trades = []
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for pair in _create_pairs(config):
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single_pair_trades = run_single_pair(
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market_data=market_data_df, price_column=price_column, pair=pair
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)
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if single_pair_trades is not None and len(single_pair_trades) > 0:
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pairs_trades.append(single_pair_trades)
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# Check if result_list has any data before concatenating
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if len(pairs_trades) == 0:
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print("No trading signals found for any pairs")
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return None
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result = pd.concat(pairs_trades, ignore_index=True)
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result["time"] = pd.to_datetime(result["time"])
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result = result.set_index("time").sort_index()
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BacktestResults.collect_single_day_results(result)
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# BacktestResults.print_single_day_results()
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if __name__ == "__main__":
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# Initialize a dictionary to store all trade results
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all_results: Dict[str, Dict[str, Any]] = {}
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# Initialize global PnL tracking variables
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# Process each data file
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price_column = CONFIG["price_column"]
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for datafile in CONFIG["datafiles"]:
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print(f"\n====== Processing {datafile} ======")
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# Clear the TRADES global dictionary and reset unrealized PnL for the new file
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BacktestResults.clear_trades()
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# Process data for this file
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try:
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market_data_df = load_market_data(
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f'{CONFIG["data_directory"]}/{datafile}', config=CONFIG
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)
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market_data_df = transform_dataframe(
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df=market_data_df, price_column=price_column
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)
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run_pairs(config=CONFIG, market_data_df=market_data_df, price_column=price_column)
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# Store results with file name as key
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filename = datafile.split("/")[-1]
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all_results[filename] = {"trades": BacktestResults.trades.copy()}
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print(f"Successfully processed {filename}")
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# No longer printing unrealized PnL since we removed that functionality
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except Exception as e:
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print(f"Error processing {datafile}: {str(e)}")
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# BacktestResults.print_results_summary(all_results)
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BacktestResults.calculate_returns(all_results)
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# Print grand totals
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BacktestResults.print_grand_totals()
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BacktestResults.print_outstanding_positions()
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