from __future__ import annotations from typing import Any, Dict, List, Optional import pandas as pd from pt_strategy.model_data_policy import ModelDataPolicy from pt_strategy.pt_market_data import ResearchMarketData from pt_strategy.pt_model import Prediction from pt_strategy.trading_pair import PairState, TradingPair class PtResearchStrategy: config_: Dict[str, Any] trading_pair_: TradingPair model_data_policy_: ModelDataPolicy pt_mkt_data_: ResearchMarketData trades_: List[pd.DataFrame] predictions_: pd.DataFrame def __init__( self, config: Dict[str, Any], datafiles: List[str], instruments: List[Dict[str, str]], ): from pt_strategy.model_data_policy import ModelDataPolicy from pt_strategy.trading_pair import TradingPair self.config_ = config self.trades_ = [] self.trading_pair_ = TradingPair(config=config, instruments=instruments) self.predictions_ = pd.DataFrame() import copy # modified config must be passed to PtMarketData config_copy = copy.deepcopy(config) config_copy["instruments"] = instruments config_copy["datafiles"] = datafiles self.pt_mkt_data_ = ResearchMarketData(config=config_copy) self.pt_mkt_data_.load() self.model_data_policy_ = ModelDataPolicy.create( config, mkt_data=self.pt_mkt_data_.market_data_df_, pair=self.trading_pair_ ) def outstanding_positions(self) -> List[Dict[str, Any]]: return list(self.trading_pair_.user_data_.get("outstanding_positions", [])) def run(self) -> None: training_minutes = self.config_.get("training_minutes", 120) market_data_series: pd.Series market_data_df = pd.DataFrame() idx = 0 while self.pt_mkt_data_.has_next(): market_data_series = self.pt_mkt_data_.get_next() new_row = pd.DataFrame([market_data_series]) market_data_df = pd.concat([market_data_df, new_row], ignore_index=True) if idx >= training_minutes: break idx += 1 assert idx >= training_minutes, "Not enough training data" while self.pt_mkt_data_.has_next(): market_data_series = self.pt_mkt_data_.get_next() new_row = pd.DataFrame([market_data_series]) market_data_df = pd.concat([market_data_df, new_row], ignore_index=True) prediction = self.trading_pair_.run( market_data_df, self.model_data_policy_.advance(mkt_data_df=market_data_df) ) self.predictions_ = pd.concat( [self.predictions_, prediction.to_df()], ignore_index=True ) assert prediction is not None trades = self._create_trades( prediction=prediction, last_row=market_data_df.iloc[-1] ) if trades is not None: self.trades_.append(trades) trades = self._handle_outstanding_positions() if trades is not None: self.trades_.append(trades) def _create_trades( self, prediction: Prediction, last_row: pd.Series ) -> Optional[pd.DataFrame]: pair = self.trading_pair_ trades = None open_threshold = self.config_["dis-equilibrium_open_trshld"] close_threshold = self.config_["dis-equilibrium_close_trshld"] scaled_disequilibrium = prediction.scaled_disequilibrium_ abs_scaled_disequilibrium = abs(scaled_disequilibrium) if pair.user_data_["state"] in [ PairState.INITIAL, PairState.CLOSE, PairState.CLOSE_POSITION, PairState.CLOSE_STOP_LOSS, PairState.CLOSE_STOP_PROFIT, ]: if abs_scaled_disequilibrium >= open_threshold: trades = self._create_open_trades( pair, row=last_row, prediction=prediction ) if trades is not None: trades["status"] = PairState.OPEN.name print(f"OPEN TRADES:\n{trades}") pair.user_data_["state"] = PairState.OPEN pair.on_open_trades(trades) elif pair.user_data_["state"] == PairState.OPEN: if abs_scaled_disequilibrium <= close_threshold: trades = self._create_close_trades( pair, row=last_row, prediction=prediction ) if trades is not None: trades["status"] = PairState.CLOSE.name print(f"CLOSE TRADES:\n{trades}") pair.user_data_["state"] = PairState.CLOSE pair.on_close_trades(trades) elif pair.to_stop_close_conditions(predicted_row=last_row): trades = self._create_close_trades(pair, row=last_row) if trades is not None: trades["status"] = pair.user_data_["stop_close_state"].name print(f"STOP CLOSE TRADES:\n{trades}") pair.user_data_["state"] = pair.user_data_["stop_close_state"] pair.on_close_trades(trades) return trades def _handle_outstanding_positions(self) -> Optional[pd.DataFrame]: trades = None pair = self.trading_pair_ # Outstanding positions if pair.user_data_["state"] == PairState.OPEN: print(f"{pair}: *** Position is NOT CLOSED. ***") # outstanding positions if self.config_["close_outstanding_positions"]: close_position_row = pd.Series(pair.market_data_.iloc[-2]) # close_position_row["disequilibrium"] = 0.0 # close_position_row["scaled_disequilibrium"] = 0.0 # close_position_row["signed_scaled_disequilibrium"] = 0.0 trades = self._create_close_trades( pair=pair, row=close_position_row, prediction=None ) if trades is not None: trades["status"] = PairState.CLOSE_POSITION.name print(f"CLOSE_POSITION TRADES:\n{trades}") pair.user_data_["state"] = PairState.CLOSE_POSITION pair.on_close_trades(trades) else: pair.add_outstanding_position( symbol=pair.symbol_a_, open_side=pair.user_data_["open_side_a"], open_px=pair.user_data_["open_px_a"], open_tstamp=pair.user_data_["open_tstamp"], last_mkt_data_row=pair.market_data_.iloc[-1], ) pair.add_outstanding_position( symbol=pair.symbol_b_, open_side=pair.user_data_["open_side_b"], open_px=pair.user_data_["open_px_b"], open_tstamp=pair.user_data_["open_tstamp"], last_mkt_data_row=pair.market_data_.iloc[-1], ) return trades def _trades_df(self) -> pd.DataFrame: types = { "time": "datetime64[ns]", "action": "string", "symbol": "string", "side": "string", "price": "float64", "disequilibrium": "float64", "scaled_disequilibrium": "float64", "signed_scaled_disequilibrium": "float64", # "pair": "object", } columns = list(types.keys()) return pd.DataFrame(columns=columns).astype(types) def _create_open_trades( self, pair: TradingPair, row: pd.Series, prediction: Prediction ) -> Optional[pd.DataFrame]: colname_a, colname_b = pair.exec_prices_colnames() tstamp = row["tstamp"] diseqlbrm = prediction.disequilibrium_ scaled_disequilibrium = prediction.scaled_disequilibrium_ px_a = row[f"{colname_a}"] px_b = row[f"{colname_b}"] # creating the trades df = self._trades_df() print(f"OPEN_TRADES: {row["tstamp"]} {scaled_disequilibrium=}") if diseqlbrm > 0: side_a = "SELL" side_b = "BUY" else: side_a = "BUY" side_b = "SELL" # save closing sides pair.user_data_["open_side_a"] = side_a # used in oustanding positions pair.user_data_["open_side_b"] = side_b pair.user_data_["open_px_a"] = px_a pair.user_data_["open_px_b"] = px_b pair.user_data_["open_tstamp"] = tstamp pair.user_data_["close_side_a"] = side_b # used for closing trades pair.user_data_["close_side_b"] = side_a # create opening trades df.loc[len(df)] = { "time": tstamp, "symbol": pair.symbol_a_, "side": side_a, "action": "OPEN", "price": px_a, "disequilibrium": diseqlbrm, "signed_scaled_disequilibrium": scaled_disequilibrium, "scaled_disequilibrium": abs(scaled_disequilibrium), # "pair": pair, } df.loc[len(df)] = { "time": tstamp, "symbol": pair.symbol_b_, "side": side_b, "action": "OPEN", "price": px_b, "disequilibrium": diseqlbrm, "scaled_disequilibrium": abs(scaled_disequilibrium), "signed_scaled_disequilibrium": scaled_disequilibrium, # "pair": pair, } return df def _create_close_trades( self, pair: TradingPair, row: pd.Series, prediction: Optional[Prediction] = None ) -> Optional[pd.DataFrame]: colname_a, colname_b = pair.exec_prices_colnames() tstamp = row["tstamp"] if prediction is not None: diseqlbrm = prediction.disequilibrium_ signed_scaled_disequilibrium = prediction.scaled_disequilibrium_ scaled_disequilibrium = abs(prediction.scaled_disequilibrium_) else: diseqlbrm = 0.0 signed_scaled_disequilibrium = 0.0 scaled_disequilibrium = 0.0 px_a = row[f"{colname_a}"] px_b = row[f"{colname_b}"] # creating the trades df = self._trades_df() # create opening trades df.loc[len(df)] = { "time": tstamp, "symbol": pair.symbol_a_, "side": pair.user_data_["close_side_a"], "action": "CLOSE", "price": px_a, "disequilibrium": diseqlbrm, "scaled_disequilibrium": scaled_disequilibrium, "signed_scaled_disequilibrium": signed_scaled_disequilibrium, # "pair": pair, } df.loc[len(df)] = { "time": tstamp, "symbol": pair.symbol_b_, "side": pair.user_data_["close_side_b"], "action": "CLOSE", "price": px_b, "disequilibrium": diseqlbrm, "scaled_disequilibrium": scaled_disequilibrium, "signed_scaled_disequilibrium": signed_scaled_disequilibrium, # "pair": pair, } del pair.user_data_["close_side_a"] del pair.user_data_["close_side_b"] del pair.user_data_["open_tstamp"] del pair.user_data_["open_px_a"] del pair.user_data_["open_px_b"] del pair.user_data_["open_side_a"] del pair.user_data_["open_side_b"] return df def day_trades(self) -> pd.DataFrame: return pd.concat(self.trades_, ignore_index=True)