progress
This commit is contained in:
Oleg Sheynin
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from abc import ABC, abstractmethod
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from enum import Enum
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from typing import Dict, Optional, cast
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import pandas as pd # type: ignore[import]
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from pt_trading.fit_method import PairState, PairsTradingFitMethod
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from pt_trading.results import BacktestResult
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from pt_trading.trading_pair import TradingPair
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NanoPerMin = 1e9
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class SlidingFit(PairsTradingFitMethod):
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def __init__(self) -> None:
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super().__init__()
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self.curr_training_start_idx_ = 0
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def run_pair(
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self, config: Dict, pair: TradingPair, bt_result: BacktestResult
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) -> Optional[pd.DataFrame]:
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print(f"***{pair}*** STARTING....")
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pair.user_data_["state"] = PairState.INITIAL
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# Initialize trades DataFrame with proper dtypes to avoid concatenation warnings
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pair.user_data_["trades"] = pd.DataFrame(columns=self.TRADES_COLUMNS).astype({
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"time": "datetime64[ns]",
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"action": "string",
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"symbol": "string",
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"price": "float64",
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"disequilibrium": "float64",
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"scaled_disequilibrium": "float64",
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"pair": "object"
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})
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pair.user_data_["is_cointegrated"] = False
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training_minutes = config["training_minutes"]
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curr_predicted_row_idx = 0
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while True:
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print(self.curr_training_start_idx_, end="\r")
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pair.get_datasets(
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training_minutes=training_minutes,
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training_start_index=self.curr_training_start_idx_,
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testing_size=1,
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)
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if len(pair.training_df_) < training_minutes:
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print(
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f"{pair}: current offset={self.curr_training_start_idx_}"
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f" * Training data length={len(pair.training_df_)} < {training_minutes}"
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" * Not enough training data. Completing the job."
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)
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break
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try:
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is_cointegrated = pair.train_pair()
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except Exception as e:
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raise RuntimeError(f"{pair}: Training failed: {str(e)}") from e
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if pair.user_data_["is_cointegrated"] != is_cointegrated:
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pair.user_data_["is_cointegrated"] = is_cointegrated
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if not is_cointegrated:
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if pair.user_data_["state"] == PairState.OPEN:
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print(
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f"{pair} {self.curr_training_start_idx_} LOST COINTEGRATION. Consider closing positions..."
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)
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else:
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print(
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f"{pair} {self.curr_training_start_idx_} IS NOT COINTEGRATED. Moving on"
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)
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else:
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print("*" * 80)
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print(
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f"Pair {pair} ({self.curr_training_start_idx_}) IS COINTEGRATED"
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)
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print("*" * 80)
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if not is_cointegrated:
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self.curr_training_start_idx_ += 1
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continue
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try:
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pair.predict()
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except Exception as e:
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raise RuntimeError(f"{pair}: Prediction failed: {str(e)}") from e
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# break
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self.curr_training_start_idx_ += 1
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curr_predicted_row_idx += 1
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self._create_trading_signals(pair, config, bt_result)
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print(f"***{pair}*** FINISHED ... {len(pair.user_data_['trades'])}")
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return pair.get_trades()
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def _create_trading_signals(
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self, pair: TradingPair, config: Dict, bt_result: BacktestResult
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) -> None:
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if pair.predicted_df_ is None:
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print(f"{pair.market_data_.iloc[0]['tstamp']} {pair}: No predicted data")
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return
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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 curr_predicted_row_idx in range(len(pair.predicted_df_)):
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pred_row = pair.predicted_df_.iloc[curr_predicted_row_idx]
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if pair.user_data_["state"] in [PairState.INITIAL, PairState.CLOSED]:
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open_trades = self._get_open_trades(
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pair, row=pred_row, open_threshold=open_threshold
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)
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if open_trades is not None:
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open_trades["status"] = "OPEN"
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print(f"OPEN TRADES:\n{open_trades}")
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pair.add_trades(open_trades)
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pair.user_data_["state"] = PairState.OPEN
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elif pair.user_data_["state"] == PairState.OPEN:
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close_trades = self._get_close_trades(
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pair, row=pred_row, close_threshold=close_threshold
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)
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if close_trades is not None:
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close_trades["status"] = "CLOSE"
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print(f"CLOSE TRADES:\n{close_trades}")
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pair.add_trades(close_trades)
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pair.user_data_["state"] = PairState.CLOSED
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# Outstanding positions
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if pair.user_data_["state"] == PairState.OPEN:
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print(
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f"{pair}: *** Position is NOT CLOSED. ***"
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)
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# outstanding positions
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# last_row_index = self.curr_training_start_idx_ + training_minutes
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if pair.predicted_df_ is not None:
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bt_result.handle_outstanding_position(
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pair=pair,
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pair_result_df=pair.predicted_df_,
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last_row_index=0,
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open_side_a=pair.user_data_["open_side_a"],
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open_side_b=pair.user_data_["open_side_b"],
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open_px_a=pair.user_data_["open_px_a"],
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open_px_b=pair.user_data_["open_px_b"],
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open_tstamp=pair.user_data_["open_tstamp"],
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)
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def _get_open_trades(
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self, pair: TradingPair, row: pd.Series, open_threshold: float
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) -> Optional[pd.DataFrame]:
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colname_a, colname_b = pair.colnames()
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assert pair.predicted_df_ is not None
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predicted_df = pair.predicted_df_
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# Check if we have any data to work with
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if len(predicted_df) == 0:
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return None
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open_row = row
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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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if open_scaled_disequilibrium < open_threshold:
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return None
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# creating the trades
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print(f"OPEN_TRADES: {row["tstamp"]} {open_scaled_disequilibrium=}")
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if open_disequilibrium > 0:
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open_side_a = "SELL"
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open_side_b = "BUY"
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close_side_a = "BUY"
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close_side_b = "SELL"
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else:
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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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# save closing sides
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pair.user_data_["open_side_a"] = open_side_a
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pair.user_data_["open_side_b"] = open_side_b
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pair.user_data_["open_px_a"] = open_px_a
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pair.user_data_["open_px_b"] = open_px_b
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pair.user_data_["open_tstamp"] = open_tstamp
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pair.user_data_["close_side_a"] = close_side_a
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pair.user_data_["close_side_b"] = close_side_b
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# create opening 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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# Create DataFrame with explicit dtypes to avoid concatenation warnings
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df = pd.DataFrame(
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trd_signal_tuples,
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columns=self.TRADES_COLUMNS,
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)
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# Ensure consistent dtypes
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return df.astype({
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"time": "datetime64[ns]",
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"action": "string",
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"symbol": "string",
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"price": "float64",
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"disequilibrium": "float64",
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"scaled_disequilibrium": "float64",
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"pair": "object"
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})
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def _get_close_trades(
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self, pair: TradingPair, row: pd.Series, close_threshold: float
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) -> Optional[pd.DataFrame]:
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colname_a, colname_b = pair.colnames()
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assert pair.predicted_df_ is not None
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if len(pair.predicted_df_) == 0:
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return None
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close_row = row
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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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close_side_a = pair.user_data_["close_side_a"]
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close_side_b = pair.user_data_["close_side_b"]
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if close_scaled_disequilibrium > close_threshold:
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return None
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trd_signal_tuples = [
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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 with explicit dtypes to avoid concatenation warnings
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df = pd.DataFrame(
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trd_signal_tuples,
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columns=self.TRADES_COLUMNS,
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)
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# Ensure consistent dtypes
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return df.astype({
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"time": "datetime64[ns]",
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"action": "string",
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"symbol": "string",
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"price": "float64",
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"disequilibrium": "float64",
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"scaled_disequilibrium": "float64",
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"pair": "object"
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})
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def reset(self) -> None:
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self.curr_training_start_idx_ = 0
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