733 lines
25 KiB
Python
733 lines
25 KiB
Python
import datetime
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import sys
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import json
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from typing import Any, Dict, List, Tuple, 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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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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# ------------------------ Configuration ------------------------
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# Default configuration
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CRYPTO_CONFIG: Dict = {
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# --- Data retrieval
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"data_directory": "./data/crypto",
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"datafiles": [
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"20250519.mktdata.ohlcv.db",
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],
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"db_table_name": "bnbspot_ohlcv_1min",
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# ----- Instruments
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"exchange_id": "BNBSPOT",
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"instrument_id_pfx": "PAIR-",
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"instruments": [
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"BTC-USDT",
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"ETH-USDT",
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"LTC-USDT",
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],
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"trading_hours": {
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"begin_session": "00:00:00",
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"end_session": "23:59:00",
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"timezone": "UTC"
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},
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# ----- Model Settings
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"price_column": "close",
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"min_required_points": 30,
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"zero_threshold": 1e-10,
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"equilibrium_threshold_open": 5.0,
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"equilibrium_threshold_close": 1.0,
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"training_minutes": 120,
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# ----- Validation
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"funding_per_pair": 2000.0, # USD
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}
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# ========================== EQUITIES
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EQT_CONFIG: Dict = {
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# --- Data retrieval
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"data_directory": "./data/equity",
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"datafiles": [
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"20250508.alpaca_sim_md.db",
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# "20250509.alpaca_sim_md.db",
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# "20250512.alpaca_sim_md.db",
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# "20250513.alpaca_sim_md.db",
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# "20250514.alpaca_sim_md.db",
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# "20250515.alpaca_sim_md.db",
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# "20250516.alpaca_sim_md.db",
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# "20250519.alpaca_sim_md.db",
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# "20250520.alpaca_sim_md.db"
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],
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"db_table_name": "md_1min_bars",
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# ----- Instruments
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"exchange_id": "ALPACA",
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"instrument_id_pfx": "STOCK-",
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"instruments": [
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"COIN",
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"GBTC",
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"HOOD",
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"MSTR",
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"PYPL",
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],
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"trading_hours": {
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"begin_session": "9:30:00",
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"end_session": "16:00:00",
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"timezone": "America/New_York"
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},
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# ----- Model Settings
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"price_column": "close",
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"min_required_points": 30,
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"zero_threshold": 1e-10,
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"equilibrium_threshold_open": 5.0,
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"equilibrium_threshold_close": 1.0,
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"training_minutes": 120,
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# ----- Validation
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"funding_per_pair": 2000.0,
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}
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# ==========================================================================
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CONFIG = EQT_CONFIG
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TRADES = {}
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TOTAL_UNREALIZED_PNL = 0.0 # Global variable to track total unrealized PnL
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TOTAL_REALIZED_PNL = 0.0 # Global variable to track total realized PnL
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OUTSTANDING_POSITIONS = [] # Global list to track outstanding positions with share quantities
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class TradingPair:
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symbol_a_: str
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symbol_b_: str
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price_column_: str
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def __init__(self, symbol_a: str, symbol_b: str, price_column: str):
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self.symbol_a_ = symbol_a
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self.symbol_b_ = symbol_b
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self.price_column_ = price_column
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def colnames(self) -> List[str]:
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return [f"{self.price_column_}_{self.symbol_a_}", f"{self.price_column_}_{self.symbol_b_}"]
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def __repr__(self) ->str:
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return f"{self.symbol_a_} & {self.symbol_b_}"
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def convert_time_to_UTC(value: str, timezone: str):
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from zoneinfo import ZoneInfo
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from datetime import datetime
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# Parse it to naive datetime object
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local_dt = datetime.strptime(value, '%Y-%m-%d %H:%M:%S')
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zinfo = ZoneInfo(timezone)
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result = local_dt.replace(tzinfo=zinfo)
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result = result.astimezone(ZoneInfo('UTC'))
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result = result.strftime('%Y-%m-%d %H:%M:%S')
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return result
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pass
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def load_market_data(datafile: str, config: Dict) -> pd.DataFrame:
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from tools.data_loader import load_sqlite_to_dataframe
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instrument_ids = ["\"" + config["instrument_id_pfx"] + instrument + "\"" for instrument in config["instruments"]]
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exchange_id = config["exchange_id"]
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query = "select tstamp"
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query += ", tstamp_ns as time_ns"
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query += ", substr(instrument_id, 7) as symbol"
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query += ", open"
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query += ", high"
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query += ", low"
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query += ", close"
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query += ", volume"
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query += ", num_trades"
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query += ", vwap"
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query += f" from {config['db_table_name']}"
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query += f" where exchange_id ='{exchange_id}'"
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query += f" and instrument_id in ({','.join(instrument_ids)})"
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df = load_sqlite_to_dataframe(db_path=datafile, query=query)
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# Trading Hours
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date_str = df["tstamp"][0][0:10]
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trading_hours = CONFIG['trading_hours']
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start_time = f"{date_str} {trading_hours['begin_session']}"
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end_time = f"{date_str} {trading_hours['end_session']}"
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start_time = convert_time_to_UTC(start_time, trading_hours["timezone"])
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end_time = convert_time_to_UTC(end_time, trading_hours["timezone"])
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# Perform boolean selection
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df = df[(df["tstamp"] >= start_time) & (df["tstamp"] <= end_time)]
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df["tstamp"] = pd.to_datetime(df["tstamp"])
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return df
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def transform_dataframe(df: pd.DataFrame, price_column: str):
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# Select only the columns we need
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df_selected = df[["tstamp", "symbol", price_column]]
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# Start with unique timestamps
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result_df: pd.DataFrame = pd.DataFrame(df_selected["tstamp"]).drop_duplicates().reset_index(drop=True)
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# For each unique symbol, add a corresponding close price column
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for symbol in df_selected["symbol"].unique():
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# Filter rows for this symbol
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df_symbol = df_selected[df_selected["symbol"] == symbol].reset_index(drop=True)
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# Create column name like "close-COIN"
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new_price_column = f"{price_column}_{symbol}"
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# Create temporary dataframe with timestamp and price
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temp_df = pd.DataFrame({
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"tstamp": df_symbol["tstamp"],
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new_price_column: df_symbol[price_column]
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})
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# Join with our result dataframe
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result_df = pd.merge(result_df, temp_df, on="tstamp", how="left")
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result_df = result_df.reset_index(drop=True) # do not dropna() since irrelevant symbol would affect dataset
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return result_df
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def get_datasets(df: pd.DataFrame, training_minutes: int, pair: TradingPair) -> Tuple[pd.DataFrame, pd.DataFrame]:
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# Training dataset
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colname_a, colname_b = pair.colnames()
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df = df[["tstamp", colname_a, colname_b]]
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df = df.dropna()
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training_df = df.iloc[:training_minutes - 1, :].copy()
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training_df.reset_index(drop=True).dropna().reset_index(drop=True)
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# Testing dataset
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testing_df = df.iloc[training_minutes:, :].copy()
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testing_df.reset_index(drop=True).dropna().reset_index(drop=True)
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return (training_df, testing_df)
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def fit_VECM(training_pair_df, pair: TradingPair):
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vecm_model = VECM(training_pair_df[pair.colnames()].reset_index(drop=True), coint_rank=1)
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vecm_fit = vecm_model.fit()
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# Check if the model converged properly
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if not hasattr(vecm_fit, "beta") or vecm_fit.beta is None:
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print(f"{pair}: VECM model failed to converge properly")
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return vecm_fit
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def create_trading_signals(vecm_fit, testing_pair_df, 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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"equilibrium",
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"pair",
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]
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next_values = 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 = vecm_fit.beta
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predicted_df["equilibrium_term"] = (
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beta[0] * predicted_df[colname_a]
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+ beta[1] * predicted_df[colname_b]
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)
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pair_result_df = pd.merge(
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testing_pair_df.reset_index(drop=True), predicted_df, left_index=True, right_index=True, suffixes=('', '_pred')
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).dropna()
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pair_result_df["equilibrium"] = (
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beta[0] * pair_result_df[colname_a]
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+ beta[1] * pair_result_df[colname_b]
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)
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pair_result_df["abs_equilibrium"] = np.abs(pair_result_df["equilibrium"])
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pair_result_df["scaled_equilibrium"] = pair_result_df["abs_equilibrium"] / (abs(beta[0]) * pair_result_df[colname_a] + abs(beta[1]) * pair_result_df[colname_b])
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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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for row_idx in range(len(pair_result_df)):
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current_abs_term = pair_result_df["abs_equilibrium"][row_idx]
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# Check if current row has sufficient equilibrium (not near-zero)
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if current_abs_term >= CONFIG["equilibrium_threshold_open"]:
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open_row_index = row_idx
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initial_abs_term = current_abs_term
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break
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# If no row with sufficient equilibrium found, skip this pair
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if open_row_index is None:
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print(f"{pair}: Insufficient divergence 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 = (
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pair_result_df["abs_equilibrium"][open_row_index:]
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# < initial_abs_term / CONFIG["equilibrium_threshold_close"]
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< CONFIG["equilibrium_threshold_close"]
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)
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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_eqlbrm = open_row["equilibrium"]
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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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global TOTAL_UNREALIZED_PNL, OUTSTANDING_POSITIONS
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last_row_index = len(pair_result_df) - 1
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last_row = pair_result_df.loc[last_row_index]
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last_tstamp = last_row["tstamp"]
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last_px_a = last_row[f"{colname_a}"]
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last_px_b = last_row[f"{colname_b}"]
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# Calculate share quantities based on $1000 funding per pair
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# Split $1000 equally between the two positions ($500 each)
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funding_per_position = CONFIG["funding_per_pair"] / 2
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shares_a = funding_per_position / open_px_a
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shares_b = funding_per_position / open_px_b
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# Calculate unrealized PnL for each position
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if open_side_a == "BUY":
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unrealized_pnl_a = (last_px_a - open_px_a) / open_px_a * 100
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unrealized_dollar_a = shares_a * (last_px_a - open_px_a)
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else: # SELL
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unrealized_pnl_a = (open_px_a - last_px_a) / open_px_a * 100
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unrealized_dollar_a = shares_a * (open_px_a - last_px_a)
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if open_side_b == "BUY":
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unrealized_pnl_b = (last_px_b - open_px_b) / open_px_b * 100
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unrealized_dollar_b = shares_b * (last_px_b - open_px_b)
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else: # SELL
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unrealized_pnl_b = (open_px_b - last_px_b) / open_px_b * 100
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unrealized_dollar_b = shares_b * (open_px_b - last_px_b)
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total_unrealized_pnl = unrealized_pnl_a + unrealized_pnl_b
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total_unrealized_dollar = unrealized_dollar_a + unrealized_dollar_b
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# Add to global total
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TOTAL_UNREALIZED_PNL += total_unrealized_pnl
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# Store outstanding positions
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OUTSTANDING_POSITIONS.append({
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'pair': str(pair),
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'symbol_a': pair.symbol_a_,
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'symbol_b': pair.symbol_b_,
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'side_a': open_side_a,
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'side_b': open_side_b,
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'shares_a': shares_a,
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'shares_b': shares_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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'current_px_a': last_px_a,
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'current_px_b': last_px_b,
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'unrealized_dollar_a': unrealized_dollar_a,
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'unrealized_dollar_b': unrealized_dollar_b,
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'total_unrealized_dollar': total_unrealized_dollar,
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'open_time': open_tstamp,
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'last_time': last_tstamp,
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'initial_abs_term': initial_abs_term,
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'current_abs_term': pair_result_df.loc[last_row_index, "abs_equilibrium"],
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'closing_threshold': initial_abs_term / CONFIG["equilibrium_threshold_close"],
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'equilibrium_ratio': pair_result_df.loc[last_row_index, "abs_equilibrium"] / (initial_abs_term / CONFIG["equilibrium_threshold_close"])
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})
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print(f"{pair}: NO CLOSE SIGNAL FOUND - Position held until end of session")
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print(f" Open: {open_tstamp} | Last: {last_tstamp}")
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print(f" {pair.symbol_a_}: {open_side_a} {shares_a:.2f} shares @ ${open_px_a:.2f} -> ${last_px_a:.2f} | Unrealized: ${unrealized_dollar_a:.2f} ({unrealized_pnl_a:.2f}%)")
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print(f" {pair.symbol_b_}: {open_side_b} {shares_b:.2f} shares @ ${open_px_b:.2f} -> ${last_px_b:.2f} | Unrealized: ${unrealized_dollar_b:.2f} ({unrealized_pnl_b:.2f}%)")
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print(f" Total Unrealized: ${total_unrealized_dollar:.2f} ({total_unrealized_pnl:.2f}%)")
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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_eqlbrm,
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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_eqlbrm,
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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_eqlbrm = close_row["equilibrium"]
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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_eqlbrm,
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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_eqlbrm,
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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_eqlbrm,
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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_eqlbrm,
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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(market_data: pd.DataFrame, price_column:str, pair: TradingPair) -> Optional[pd.DataFrame]:
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colname_a = f"{price_column}_{pair.symbol_a_}"
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colname_b = f"{price_column}_{pair.symbol_b_}"
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training_pair_df, testing_pair_df = get_datasets(df=market_data, training_minutes=CONFIG["training_minutes"], pair=pair)
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|
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# Check if we have enough data points for a meaningful analysis
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|
min_required_points = CONFIG[
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"min_required_points"
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] # Minimum number of points for a reasonable VECM model
|
|
if len(training_pair_df) < min_required_points:
|
|
print(
|
|
f"{pair}: Not enough data points for analysis. Found {len(training_pair_df)}, need at least {min_required_points}"
|
|
)
|
|
return None
|
|
|
|
# Check for non-finite values
|
|
if not np.isfinite(training_pair_df).all().all():
|
|
print(f"{pair}: Data contains non-finite values (NaN or inf)")
|
|
return None
|
|
|
|
# Fit the VECM
|
|
try:
|
|
vecm_fit = fit_VECM(training_pair_df, pair=pair)
|
|
except Exception as e:
|
|
print(f"{pair}: VECM fitting failed: {str(e)}")
|
|
return None
|
|
|
|
# Add safeguard against division by zero
|
|
if (
|
|
abs(vecm_fit.beta[1]) < CONFIG["zero_threshold"]
|
|
): # Small threshold to avoid division by very small numbers
|
|
print(f"{pair}: Skipping due to near-zero beta[1] value: {vecm_fit.beta[1]}")
|
|
return None
|
|
|
|
try:
|
|
pair_trades = create_trading_signals(
|
|
vecm_fit=vecm_fit,
|
|
testing_pair_df=testing_pair_df,
|
|
pair=pair,
|
|
)
|
|
except Exception as e:
|
|
print(f"{pair}: Prediction failed: {str(e)}")
|
|
return None
|
|
|
|
return pair_trades
|
|
|
|
def add_trade(pair_nm, symbol, action, price):
|
|
pair_nm = str(pair_nm)
|
|
|
|
|
|
if pair_nm not in TRADES:
|
|
TRADES[pair_nm] = {symbol: []}
|
|
if symbol not in TRADES[pair_nm]:
|
|
TRADES[pair_nm][symbol] = []
|
|
TRADES[pair_nm][symbol].append((action, price))
|
|
|
|
def collect_single_day_results(result):
|
|
if result is None:
|
|
return
|
|
|
|
print("\n -------------- Suggested Trades ")
|
|
print(result)
|
|
|
|
for row in result.itertuples():
|
|
action = row.action
|
|
symbol = row.symbol
|
|
price = row.price
|
|
add_trade(pair_nm=row.pair, action=action, symbol=symbol, price=price)
|
|
|
|
def print_single_day_results(result):
|
|
for pair, symbols in TRADES.items():
|
|
print(f"\n--- {pair} ---")
|
|
for symbol, trades in symbols.items():
|
|
for side, price in trades:
|
|
print(f"{symbol} {side} at ${price}")
|
|
|
|
def print_results_suummary(all_results):
|
|
# Summary of all processed files
|
|
print("\n====== Summary of All Processed Files ======")
|
|
for filename, data in all_results.items():
|
|
trade_count = sum(
|
|
len(trades)
|
|
for symbol_trades in data["trades"].values()
|
|
for trades in symbol_trades.values()
|
|
)
|
|
print(f"{filename}: {trade_count} trades")
|
|
|
|
|
|
def calculate_returns(all_results: Dict):
|
|
global TOTAL_REALIZED_PNL
|
|
print("\n====== Returns By Day and Pair ======")
|
|
|
|
for filename, data in all_results.items():
|
|
day_return = 0
|
|
print(f"\n--- {filename} ---")
|
|
|
|
# Process each pair
|
|
for pair, symbols in data["trades"].items():
|
|
pair_return = 0
|
|
pair_trades = []
|
|
|
|
# Calculate individual symbol returns in the pair
|
|
for symbol, trades in symbols.items():
|
|
if len(trades) >= 2: # Need at least entry and exit
|
|
# Get entry and exit trades
|
|
entry_action, entry_price = trades[0]
|
|
exit_action, exit_price = trades[1]
|
|
|
|
# Calculate return based on action
|
|
symbol_return = 0
|
|
if entry_action == "BUY" and exit_action == "SELL":
|
|
# Long position
|
|
symbol_return = (exit_price - entry_price) / entry_price * 100
|
|
elif entry_action == "SELL" and exit_action == "BUY":
|
|
# Short position
|
|
symbol_return = (entry_price - exit_price) / entry_price * 100
|
|
|
|
pair_trades.append(
|
|
(
|
|
symbol,
|
|
entry_action,
|
|
entry_price,
|
|
exit_action,
|
|
exit_price,
|
|
symbol_return,
|
|
)
|
|
)
|
|
pair_return += symbol_return
|
|
|
|
# Print pair returns
|
|
if pair_trades:
|
|
print(f" {pair}:")
|
|
for (
|
|
symbol,
|
|
entry_action,
|
|
entry_price,
|
|
exit_action,
|
|
exit_price,
|
|
symbol_return,
|
|
) in pair_trades:
|
|
print(
|
|
f" {symbol}: {entry_action} @ ${entry_price:.2f}, {exit_action} @ ${exit_price:.2f}, Return: {symbol_return:.2f}%"
|
|
)
|
|
print(f" Pair Total Return: {pair_return:.2f}%")
|
|
day_return += pair_return
|
|
|
|
# Print day total return and add to global realized PnL
|
|
if day_return != 0:
|
|
print(f" Day Total Return: {day_return:.2f}%")
|
|
TOTAL_REALIZED_PNL += day_return
|
|
|
|
def run_pairs(summaries_df: pd.DataFrame, price_column: str) -> None:
|
|
|
|
result_df = transform_dataframe(df=summaries_df, price_column=price_column)
|
|
|
|
stock_price_columns = [
|
|
column
|
|
for column in result_df.columns
|
|
if column.startswith(f"{price_column}_")
|
|
]
|
|
|
|
# Find the starting indices for A and B
|
|
all_indexes = range(len(stock_price_columns))
|
|
unique_index_pairs = [(i, j) for i in all_indexes for j in all_indexes if i < j]
|
|
|
|
pairs_trades = []
|
|
for a_index, b_index in unique_index_pairs:
|
|
# Get the actual variable names
|
|
colname_a = stock_price_columns[a_index]
|
|
colname_b = stock_price_columns[b_index]
|
|
|
|
symbol_a = colname_a[len(f"{price_column}-") :]
|
|
symbol_b = colname_b[len(f"{price_column}-") :]
|
|
pair = TradingPair(symbol_a, symbol_b, price_column)
|
|
|
|
single_pair_trades = run_single_pair(market_data=result_df, price_column=price_column, pair=pair)
|
|
if len(single_pair_trades) > 0:
|
|
pairs_trades.append(single_pair_trades)
|
|
# Check if result_list has any data before concatenating
|
|
if len(pairs_trades) == 0:
|
|
print("No trading signals found for any pairs")
|
|
return None
|
|
|
|
result = pd.concat(pairs_trades, ignore_index=True)
|
|
result["time"] = pd.to_datetime(result["time"])
|
|
result = result.set_index("time").sort_index()
|
|
|
|
collect_single_day_results(result)
|
|
# print_single_day_results(result)
|
|
|
|
def print_outstanding_positions():
|
|
"""Print all outstanding positions with share quantities and unrealized PnL"""
|
|
if not OUTSTANDING_POSITIONS:
|
|
print("\n====== NO OUTSTANDING POSITIONS ======")
|
|
return
|
|
|
|
print(f"\n====== OUTSTANDING POSITIONS ======")
|
|
print(f"{'Pair':<15} {'Symbol':<6} {'Side':<4} {'Shares':<10} {'Open $':<8} {'Current $':<10} {'Unrealized $':<12} {'%':<8} {'Close Eq':<10}")
|
|
print("-" * 105)
|
|
|
|
total_unrealized_dollar = 0.0
|
|
|
|
for pos in OUTSTANDING_POSITIONS:
|
|
# Print position A
|
|
print(f"{pos['pair']:<15} {pos['symbol_a']:<6} {pos['side_a']:<4} {pos['shares_a']:<10.2f} {pos['open_px_a']:<8.2f} {pos['current_px_a']:<10.2f} {pos['unrealized_dollar_a']:<12.2f} {pos['unrealized_dollar_a']/500*100:<8.2f} {'':<10}")
|
|
|
|
# Print position B
|
|
print(f"{'':<15} {pos['symbol_b']:<6} {pos['side_b']:<4} {pos['shares_b']:<10.2f} {pos['open_px_b']:<8.2f} {pos['current_px_b']:<10.2f} {pos['unrealized_dollar_b']:<12.2f} {pos['unrealized_dollar_b']/500*100:<8.2f} {'':<10}")
|
|
|
|
# Print pair totals with equilibrium info
|
|
equilibrium_status = "CLOSE" if pos['current_abs_term'] < pos['closing_threshold'] else f"{pos['equilibrium_ratio']:.2f}x"
|
|
print(f"{'':<15} {'PAIR':<6} {'TOT':<4} {'':<10} {'':<8} {'':<10} {pos['total_unrealized_dollar']:<12.2f} {pos['total_unrealized_dollar']/1000*100:<8.2f} {equilibrium_status:<10}")
|
|
|
|
# Print equilibrium details
|
|
print(f"{'':<15} {'EQ':<6} {'INFO':<4} {'':<10} {'':<8} {'':<10} {'Curr:':<6}{pos['current_abs_term']:<6.4f} {'Thresh:':<7}{pos['closing_threshold']:<6.4f} {'':<10}")
|
|
print("-" * 105)
|
|
|
|
total_unrealized_dollar += pos['total_unrealized_dollar']
|
|
|
|
print(f"{'TOTAL OUTSTANDING':<80} ${total_unrealized_dollar:<12.2f}")
|
|
|
|
if __name__ == "__main__":
|
|
# Initialize a dictionary to store all trade results
|
|
all_results = {}
|
|
|
|
# Initialize global PnL tracking variables
|
|
TOTAL_REALIZED_PNL = 0.0
|
|
TOTAL_UNREALIZED_PNL = 0.0
|
|
OUTSTANDING_POSITIONS = []
|
|
|
|
# Process each data file
|
|
price_column = CONFIG["price_column"]
|
|
for datafile in CONFIG["datafiles"]:
|
|
print(f"\n====== Processing {datafile} ======")
|
|
|
|
# Clear the TRADES global dictionary and reset unrealized PnL for the new file
|
|
TRADES.clear()
|
|
TOTAL_UNREALIZED_PNL = 0.0
|
|
TOTAL_REALIZED_PNL = 0.0
|
|
|
|
# Process data for this file
|
|
try:
|
|
run_pairs(
|
|
summaries_df=load_market_data(f'{CONFIG["data_directory"]}/{datafile}', config=CONFIG),
|
|
price_column=price_column
|
|
)
|
|
|
|
# Store results with file name as key
|
|
filename = datafile.split("/")[-1]
|
|
all_results[filename] = {"trades": TRADES.copy()}
|
|
|
|
print(f"Successfully processed {filename}")
|
|
|
|
# Print total unrealized PnL for this file
|
|
if TOTAL_UNREALIZED_PNL != 0:
|
|
print(f"\n====== TOTAL UNREALIZED PnL for {filename}: {TOTAL_UNREALIZED_PNL:.2f}% ======")
|
|
else:
|
|
print(f"\n====== No unrealized positions for {filename} ======")
|
|
|
|
except Exception as e:
|
|
print(f"Error processing {datafile}: {str(e)}")
|
|
|
|
# print_results_suummary(all_results)
|
|
calculate_returns(all_results)
|
|
|
|
# Print grand totals
|
|
print(f"\n====== GRAND TOTALS ACROSS ALL PAIRS ======")
|
|
print(f"Total Realized PnL: {TOTAL_REALIZED_PNL:.2f}%")
|
|
print(f"Total Unrealized PnL: {TOTAL_UNREALIZED_PNL:.2f}%")
|
|
print(f"Combined Total PnL: {TOTAL_REALIZED_PNL + TOTAL_UNREALIZED_PNL:.2f}%")
|
|
|
|
print_outstanding_positions()
|