progress
This commit is contained in:
parent
73b7fa1aaf
commit
da6ccf2bfb
@ -1,95 +0,0 @@
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
|
||||
# ------------------------ Configuration ------------------------
|
||||
# Default configuration
|
||||
CRYPTO_CONFIG: Dict = {
|
||||
"security_type": "CRYPTO",
|
||||
# --- Data retrieval
|
||||
"data_directory": "./data/crypto",
|
||||
"datafiles": [
|
||||
# "20250519.mktdata.ohlcv.db",
|
||||
# "20250520.mktdata.ohlcv.db",
|
||||
# "20250521.mktdata.ohlcv.db",
|
||||
# "20250522.mktdata.ohlcv.db",
|
||||
# "20250523.mktdata.ohlcv.db",
|
||||
# "20250524.mktdata.ohlcv.db",
|
||||
"20250525.mktdata.ohlcv.db",
|
||||
],
|
||||
"db_table_name": "bnbspot_ohlcv_1min",
|
||||
# ----- Instruments
|
||||
"exchange_id": "BNBSPOT",
|
||||
"instrument_id_pfx": "PAIR-",
|
||||
"instruments": [
|
||||
"BTC-USDT",
|
||||
"BCH-USDT",
|
||||
"ETH-USDT",
|
||||
"LTC-USDT",
|
||||
"XRP-USDT",
|
||||
"ADA-USDT",
|
||||
"SOL-USDT",
|
||||
"DOT-USDT",
|
||||
],
|
||||
"trading_hours": {
|
||||
"begin_session": "00:00:00",
|
||||
"end_session": "23:59:00",
|
||||
"timezone": "UTC",
|
||||
},
|
||||
# ----- Model Settings
|
||||
"price_column": "close",
|
||||
"min_required_points": 30,
|
||||
"zero_threshold": 1e-10,
|
||||
|
||||
"dis-equilibrium_open_trshld": 2.0,
|
||||
"dis-equilibrium_close_trshld": 0.5,
|
||||
|
||||
# "training_minutes": 120,
|
||||
"training_minutes": 60,
|
||||
# ----- Validation
|
||||
"funding_per_pair": 2000.0, # USD
|
||||
}
|
||||
|
||||
# ========================== EQUITIES
|
||||
EQT_CONFIG: Dict = {
|
||||
# --- Data retrieval
|
||||
"security_type": "EQUITY",
|
||||
"data_directory": "./data/equity",
|
||||
"datafiles": [
|
||||
# "20250508.alpaca_sim_md.db",
|
||||
# "20250509.alpaca_sim_md.db",
|
||||
"20250512.alpaca_sim_md.db",
|
||||
# "20250513.alpaca_sim_md.db",
|
||||
# "20250514.alpaca_sim_md.db",
|
||||
# "20250515.alpaca_sim_md.db",
|
||||
# "20250516.alpaca_sim_md.db",
|
||||
# "20250519.alpaca_sim_md.db",
|
||||
# "20250520.alpaca_sim_md.db"
|
||||
],
|
||||
"db_table_name": "md_1min_bars",
|
||||
# ----- Instruments
|
||||
"exchange_id": "ALPACA",
|
||||
"instrument_id_pfx": "STOCK-",
|
||||
"instruments": [
|
||||
"COIN",
|
||||
"GBTC",
|
||||
"HOOD",
|
||||
"MSTR",
|
||||
"PYPL",
|
||||
],
|
||||
"trading_hours": {
|
||||
"begin_session": "9:30:00",
|
||||
"end_session": "16:00:00",
|
||||
"timezone": "America/New_York",
|
||||
},
|
||||
# ----- Model Settings
|
||||
"price_column": "close",
|
||||
"min_required_points": 30,
|
||||
"zero_threshold": 1e-10,
|
||||
"dis-equilibrium_open_trshld": 2.0,
|
||||
"dis-equilibrium_close_trshld": 0.5,
|
||||
"training_minutes": 120,
|
||||
# ----- Validation
|
||||
"funding_per_pair": 2000.0,
|
||||
}
|
||||
|
||||
|
||||
@ -1,28 +1,112 @@
|
||||
from abc import ABC, abstractmethod
|
||||
import sys
|
||||
|
||||
from typing import Any, Dict, List, Optional
|
||||
from typing import Any, Dict, List
|
||||
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
|
||||
# ============= statsmodels ===================
|
||||
from statsmodels.tsa.vector_ar.vecm import VECM
|
||||
|
||||
from backtest_configs import CRYPTO_CONFIG
|
||||
from strategies import StaticFitStrategy
|
||||
from strategies import SlidingFitStrategy, StaticFitStrategy
|
||||
from tools.data_loader import load_market_data
|
||||
from tools.trading_pair import TradingPair
|
||||
from results import BacktestResult
|
||||
|
||||
NanoPerMin = 1e9
|
||||
UNSET_FLOAT: float = sys.float_info.max
|
||||
UNSET_INT: int = sys.maxsize
|
||||
|
||||
# ------------------------ Configuration ------------------------
|
||||
# Default configuration
|
||||
CRYPTO_CONFIG: Dict = {
|
||||
"security_type": "CRYPTO",
|
||||
# --- Data retrieval
|
||||
"data_directory": "./data/crypto",
|
||||
"datafiles": [
|
||||
"20250519.mktdata.ohlcv.db",
|
||||
# "20250520.mktdata.ohlcv.db",
|
||||
# "20250521.mktdata.ohlcv.db",
|
||||
# "20250522.mktdata.ohlcv.db",
|
||||
# "20250523.mktdata.ohlcv.db",
|
||||
# "20250524.mktdata.ohlcv.db",
|
||||
# "20250525.mktdata.ohlcv.db",
|
||||
],
|
||||
"db_table_name": "bnbspot_ohlcv_1min",
|
||||
# ----- Instruments
|
||||
"exchange_id": "BNBSPOT",
|
||||
"instrument_id_pfx": "PAIR-",
|
||||
"instruments": [
|
||||
"BTC-USDT",
|
||||
"BCH-USDT",
|
||||
"ETH-USDT",
|
||||
"LTC-USDT",
|
||||
"XRP-USDT",
|
||||
"ADA-USDT",
|
||||
"SOL-USDT",
|
||||
"DOT-USDT",
|
||||
],
|
||||
"trading_hours": {
|
||||
"begin_session": "00:00:00",
|
||||
"end_session": "23:59:00",
|
||||
"timezone": "UTC",
|
||||
},
|
||||
# ----- Model Settings
|
||||
"price_column": "close",
|
||||
"min_required_points": 30,
|
||||
"zero_threshold": 1e-10,
|
||||
|
||||
"dis-equilibrium_open_trshld": 2.0,
|
||||
"dis-equilibrium_close_trshld": 0.5,
|
||||
|
||||
# "training_minutes": 120,
|
||||
"training_minutes": 120,
|
||||
# ----- Validation
|
||||
"funding_per_pair": 2000.0, # USD
|
||||
}
|
||||
|
||||
# ========================== EQUITIES
|
||||
EQT_CONFIG: Dict = {
|
||||
# --- Data retrieval
|
||||
"security_type": "EQUITY",
|
||||
"data_directory": "./data/equity",
|
||||
"datafiles": [
|
||||
"20250508.alpaca_sim_md.db",
|
||||
# "20250509.alpaca_sim_md.db",
|
||||
# "20250512.alpaca_sim_md.db",
|
||||
# "20250513.alpaca_sim_md.db",
|
||||
# "20250514.alpaca_sim_md.db",
|
||||
# "20250515.alpaca_sim_md.db",
|
||||
# "20250516.alpaca_sim_md.db",
|
||||
# "20250519.alpaca_sim_md.db",
|
||||
# "20250520.alpaca_sim_md.db"
|
||||
],
|
||||
"db_table_name": "md_1min_bars",
|
||||
# ----- Instruments
|
||||
"exchange_id": "ALPACA",
|
||||
"instrument_id_pfx": "STOCK-",
|
||||
"instruments": [
|
||||
"COIN",
|
||||
"GBTC",
|
||||
"HOOD",
|
||||
"MSTR",
|
||||
"PYPL",
|
||||
],
|
||||
"trading_hours": {
|
||||
"begin_session": "9:30:00",
|
||||
"end_session": "16:00:00",
|
||||
"timezone": "America/New_York",
|
||||
},
|
||||
# ----- Model Settings
|
||||
"price_column": "close",
|
||||
"min_required_points": 30,
|
||||
"zero_threshold": 1e-10,
|
||||
"dis-equilibrium_open_trshld": 2.0,
|
||||
"dis-equilibrium_close_trshld": 0.5,
|
||||
"training_minutes": 120,
|
||||
# ----- Validation
|
||||
"funding_per_pair": 2000.0,
|
||||
}
|
||||
|
||||
|
||||
CONFIG = CRYPTO_CONFIG
|
||||
# CONFIG = EQT_CONFIG
|
||||
# CONFIG = CRYPTO_CONFIG
|
||||
CONFIG = EQT_CONFIG
|
||||
STRATEGY = StaticFitStrategy()
|
||||
|
||||
# CONFIG = CRYPTO_CONFIG
|
||||
# STRATEGY = SlidingFitStrategy()
|
||||
|
||||
def run_all_pairs(config: Dict, datafile: str, price_column: str, bt_result: BacktestResult) -> None:
|
||||
|
||||
@ -47,9 +131,8 @@ def run_all_pairs(config: Dict, datafile: str, price_column: str, bt_result: Bac
|
||||
|
||||
|
||||
pairs_trades = []
|
||||
strategy = StaticFitStrategy()
|
||||
for pair in _create_pairs(config):
|
||||
single_pair_trades = strategy.run_pair(pair=pair, config=CONFIG, bt_result=bt_result)
|
||||
single_pair_trades = STRATEGY.run_pair(pair=pair, config=CONFIG, bt_result=bt_result)
|
||||
if single_pair_trades is not None and len(single_pair_trades) > 0:
|
||||
pairs_trades.append(single_pair_trades)
|
||||
# Check if result_list has any data before concatenating
|
||||
@ -99,6 +182,7 @@ def main() -> None:
|
||||
|
||||
# BacktestResults.print_results_summary(all_results)
|
||||
bt_results.calculate_returns(all_results)
|
||||
|
||||
# Print grand totals
|
||||
bt_results.print_grand_totals()
|
||||
bt_results.print_outstanding_positions()
|
||||
|
||||
@ -1,4 +1,5 @@
|
||||
from abc import ABC, abstractmethod
|
||||
from enum import Enum
|
||||
import sys
|
||||
|
||||
from typing import Dict, Optional
|
||||
@ -199,15 +200,29 @@ class StaticFitStrategy(PairsTradingStrategy):
|
||||
columns=self.TRADES_COLUMNS,
|
||||
)
|
||||
|
||||
class PairState(Enum):
|
||||
INITIAL = 1
|
||||
OPEN = 2
|
||||
CLOSED = 3
|
||||
|
||||
class SlidingFitStrategy(PairsTradingStrategy):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.curr_training_start_idx_ = 0
|
||||
|
||||
def run_pair(self, config: Dict, pair: TradingPair, bt_result: BacktestResult) -> Optional[pd.DataFrame]:
|
||||
pair.user_data_['is_position_open'] = False
|
||||
print(f"***{pair}*** STARTING....")
|
||||
|
||||
pair.user_data_['state'] = PairState.INITIAL
|
||||
pair.user_data_["trades"] = pd.DataFrame(columns=self.TRADES_COLUMNS)
|
||||
pair.user_data_["is_cointegrated"] = False
|
||||
|
||||
open_threshold = config["dis-equilibrium_open_trshld"]
|
||||
close_threshold = config["dis-equilibrium_open_trshld"]
|
||||
|
||||
training_minutes = config["training_minutes"]
|
||||
while True:
|
||||
print(self.curr_training_start_idx_, end='\r')
|
||||
pair.get_datasets(
|
||||
training_minutes=training_minutes,
|
||||
training_start_index=self.curr_training_start_idx_,
|
||||
@ -215,26 +230,175 @@ class SlidingFitStrategy(PairsTradingStrategy):
|
||||
)
|
||||
|
||||
if len(pair.training_df_) < training_minutes:
|
||||
print(f"{pair}: Not enough training data. Completing the job.")
|
||||
print(f"{pair}: {self.curr_training_start_idx_} Not enough training data. Completing the job.")
|
||||
if pair.user_data_["state"] == PairState.OPEN:
|
||||
print(f"{pair}: {self.curr_training_start_idx_} Position is not closed.")
|
||||
# outstanding positions
|
||||
# last_row_index = self.curr_training_start_idx_ + training_minutes
|
||||
|
||||
bt_result.handle_outstanding_position(
|
||||
pair=pair,
|
||||
pair_result_df=pair.predicted_df_,
|
||||
last_row_index=0,
|
||||
open_side_a=pair.user_data_["open_side_a"],
|
||||
open_side_b=pair.user_data_["open_side_b"],
|
||||
open_px_a=pair.user_data_["open_px_a"],
|
||||
open_px_b=pair.user_data_["open_px_b"],
|
||||
open_tstamp=pair.user_data_["open_tstamp"],
|
||||
)
|
||||
break
|
||||
|
||||
try:
|
||||
is_cointegrated = pair.train_pair()
|
||||
if not is_cointegrated:
|
||||
print(f"{pair} IS NOT COINTEGRATED")
|
||||
return None
|
||||
except Exception as e:
|
||||
print(f"{pair}: Training failed: {str(e)}")
|
||||
return None
|
||||
raise Exception(f"{pair}: Training failed: {str(e)}") from e
|
||||
|
||||
if pair.user_data_["is_cointegrated"] != is_cointegrated:
|
||||
pair.user_data_["is_cointegrated"] = is_cointegrated
|
||||
if not is_cointegrated:
|
||||
if pair.user_data_["state"] == PairState.OPEN:
|
||||
print(f"{pair} {self.curr_training_start_idx_} LOST COINTEGRATION. Consider closing positions...")
|
||||
else:
|
||||
print(f"{pair} {self.curr_training_start_idx_} IS NOT COINTEGRATED. Moving on")
|
||||
else:
|
||||
print('*' * 80)
|
||||
print(f"Pair {pair} ({self.curr_training_start_idx_}) IS COINTEGRATED")
|
||||
print('*' * 80)
|
||||
if not is_cointegrated:
|
||||
self.curr_training_start_idx_ += 1
|
||||
continue
|
||||
|
||||
try:
|
||||
pair.predict()
|
||||
except Exception as e:
|
||||
print(f"{pair}: Prediction failed: {str(e)}")
|
||||
return None
|
||||
raise Exception(f"{pair}: Prediction failed: {str(e)}") from e
|
||||
|
||||
if pair.user_data_["state"] == PairState.INITIAL:
|
||||
|
||||
open_trades = self._get_open_trades(pair, open_threshold=open_threshold)
|
||||
if open_trades is not None:
|
||||
pair.user_data_["trades"] = open_trades
|
||||
pair.user_data_["state"] = PairState.OPEN
|
||||
elif pair.user_data_["state"] == PairState.OPEN:
|
||||
close_trades = self._get_close_trades(pair, close_threshold=close_threshold)
|
||||
if close_trades is not None:
|
||||
pair.user_data_["trades"] = pd.concat([pair.user_data_["trades"], close_trades], ignore_index=True)
|
||||
pair.user_data_["state"] = PairState.CLOSED
|
||||
break
|
||||
|
||||
self.curr_training_start_idx_ += 1
|
||||
|
||||
print(f"***{pair}*** FINISHED ... {len(pair.user_data_['trades'])}")
|
||||
return pair.user_data_["trades"]
|
||||
|
||||
def _get_open_trades(self, pair: TradingPair, open_threshold: float) -> Optional[pd.DataFrame]:
|
||||
colname_a, colname_b = pair.colnames()
|
||||
|
||||
predicted_df = pair.predicted_df_
|
||||
|
||||
open_row = predicted_df.loc[0]
|
||||
open_tstamp = open_row["tstamp"]
|
||||
open_disequilibrium = open_row["disequilibrium"]
|
||||
open_scaled_disequilibrium = open_row["scaled_disequilibrium"]
|
||||
open_px_a = open_row[f"{colname_a}"]
|
||||
open_px_b = open_row[f"{colname_b}"]
|
||||
|
||||
if open_scaled_disequilibrium < open_threshold:
|
||||
return None
|
||||
|
||||
# creating the trades
|
||||
if open_disequilibrium > 0:
|
||||
open_side_a = "SELL"
|
||||
open_side_b = "BUY"
|
||||
close_side_a = "BUY"
|
||||
close_side_b = "SELL"
|
||||
else:
|
||||
open_side_a = "BUY"
|
||||
open_side_b = "SELL"
|
||||
close_side_a = "SELL"
|
||||
close_side_b = "BUY"
|
||||
|
||||
# save closing sides
|
||||
pair.user_data_["open_side_a"] = open_side_a
|
||||
pair.user_data_["open_side_b"] = open_side_b
|
||||
pair.user_data_["open_px_a"] = open_px_a
|
||||
pair.user_data_["open_px_b"] = open_px_b
|
||||
|
||||
pair.user_data_["open_tstamp"] = open_tstamp
|
||||
|
||||
pair.user_data_["close_side_a"] = close_side_a
|
||||
pair.user_data_["close_side_b"] = close_side_b
|
||||
|
||||
|
||||
# create opening trades
|
||||
trd_signal_tuples = [
|
||||
(
|
||||
open_tstamp,
|
||||
open_side_a,
|
||||
pair.symbol_a_,
|
||||
open_px_a,
|
||||
open_disequilibrium,
|
||||
open_scaled_disequilibrium,
|
||||
pair,
|
||||
),
|
||||
(
|
||||
open_tstamp,
|
||||
open_side_b,
|
||||
pair.symbol_b_,
|
||||
open_px_b,
|
||||
open_disequilibrium,
|
||||
open_scaled_disequilibrium,
|
||||
pair,
|
||||
),
|
||||
]
|
||||
return pd.DataFrame(
|
||||
trd_signal_tuples,
|
||||
columns=self.TRADES_COLUMNS,
|
||||
)
|
||||
|
||||
def _get_close_trades(self, pair: TradingPair, close_threshold: float) -> Optional[pd.DataFrame]:
|
||||
colname_a, colname_b = pair.colnames()
|
||||
|
||||
close_row = pair.predicted_df_.loc[0]
|
||||
close_tstamp = close_row["tstamp"]
|
||||
close_disequilibrium = close_row["disequilibrium"]
|
||||
close_scaled_disequilibrium = close_row["scaled_disequilibrium"]
|
||||
close_px_a = close_row[f"{colname_a}"]
|
||||
close_px_b = close_row[f"{colname_b}"]
|
||||
|
||||
close_side_a = pair.user_data_["close_side_a"]
|
||||
close_side_b = pair.user_data_["close_side_b"]
|
||||
|
||||
if close_scaled_disequilibrium > close_threshold:
|
||||
return None
|
||||
|
||||
trd_signal_tuples = [
|
||||
(
|
||||
close_tstamp,
|
||||
close_side_a,
|
||||
pair.symbol_a_,
|
||||
close_px_a,
|
||||
close_disequilibrium,
|
||||
close_scaled_disequilibrium,
|
||||
pair,
|
||||
),
|
||||
(
|
||||
close_tstamp,
|
||||
close_side_b,
|
||||
pair.symbol_b_,
|
||||
close_px_b,
|
||||
close_disequilibrium,
|
||||
close_scaled_disequilibrium,
|
||||
pair,
|
||||
),
|
||||
]
|
||||
|
||||
# Add tuples to data frame
|
||||
return pd.DataFrame(
|
||||
trd_signal_tuples,
|
||||
columns=self.TRADES_COLUMNS,
|
||||
)
|
||||
|
||||
|
||||
pair_trades = self.create_trading_signals(pair=pair, config=config, result=bt_result)
|
||||
|
||||
return pair_trades
|
||||
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
|
||||
from typing import List, Optional
|
||||
from typing import Any, Dict, List, Optional
|
||||
import pandas as pd
|
||||
from statsmodels.tsa.vector_ar.vecm import VECM
|
||||
|
||||
@ -17,19 +17,22 @@ class TradingPair:
|
||||
|
||||
vecm_fit_: Optional[VECM]
|
||||
|
||||
user_data_: Dict[str, Any]
|
||||
|
||||
def __init__(self, market_data: pd.DataFrame, symbol_a: str, symbol_b: str, price_column: str):
|
||||
self.symbol_a_ = symbol_a
|
||||
self.symbol_b_ = symbol_b
|
||||
self.price_column_ = price_column
|
||||
self.market_data_ = self._transform_dataframe(market_data)[["tstamp"] + self.colnames()]
|
||||
|
||||
|
||||
self.training_mu_ = None
|
||||
self.training_std_ = None
|
||||
self.training_df_ = None
|
||||
self.testing_df_ = None
|
||||
self.vecm_fit_ = None
|
||||
|
||||
self.user_data_ = {}
|
||||
|
||||
def _transform_dataframe(self, df: pd.DataFrame):
|
||||
# Select only the columns we need
|
||||
df_selected = df[["tstamp", "symbol", self.price_column_]]
|
||||
@ -57,7 +60,9 @@ class TradingPair:
|
||||
|
||||
return result_df
|
||||
def get_datasets(self, training_minutes: int, training_start_index: int = 0, testing_size: Optional[int] = None) -> None:
|
||||
self.training_df_ = self.market_data_.iloc[training_start_index:training_minutes - 1, :].copy()
|
||||
|
||||
testing_start_index = training_start_index + training_minutes
|
||||
self.training_df_ = self.market_data_.iloc[training_start_index:testing_start_index, :].copy()
|
||||
self.training_df_ = self.training_df_.dropna().reset_index(drop=True)
|
||||
|
||||
testing_start_index = training_start_index + training_minutes
|
||||
@ -101,7 +106,7 @@ class TradingPair:
|
||||
return False
|
||||
pass
|
||||
|
||||
print('*' * 80 + '\n' + f"**************** {self} IS COINTEGRATED ****************\n" + '*' * 80)
|
||||
# print('*' * 80 + '\n' + f"**************** {self} IS COINTEGRATED ****************\n" + '*' * 80)
|
||||
self.fit_VECM()
|
||||
diseq_series = self.training_df_[self.colnames()] @ self.vecm_fit_.beta
|
||||
self.training_mu_ = diseq_series.mean().iloc[0]
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user