from __future__ import annotations from functools import partial from typing import Any, Dict, List, Optional import pandas as pd from cvttpy_base.settings.cvtt_types import JsonDictT from cvttpy_base.tools.base import NamedObject from cvttpy_base.tools.logger import Log from cvtt_client.mkt_data import CvttPricerWebSockClient, CvttPricesSubscription, MessageTypeT, SubscriptionIdT from pt_strategy.model_data_policy import ModelDataPolicy from pt_strategy.pt_market_data import PtMarketData, RealTimeMarketData from pt_strategy.pt_model import Prediction from pt_strategy.trading_pair import PairState, TradingPair ''' --config=pair.cfg --pair=PAIR-BTC-USDT:COINBASE_AT,PAIR-ETH-USDT:COINBASE_AT ''' class PtMktDataClient(NamedObject): live_strategy_: PtLiveStrategy pricer_client_: CvttPricerWebSockClient subscriptions_: List[CvttPricesSubscription] def __init__(self, live_strategy: PtLiveStrategy): self.live_strategy_ = live_strategy async def start(self, subscription: CvttPricesSubscription) -> None: pricer_url = self.live_strategy_.config_.get("pricer_url", None) #, "ws://localhost:12346/ws") assert pricer_url is not None, "pricer_url is not found in config" self.pricer_client_ = CvttPricerWebSockClient(url=pricer_url) await self._subscribe() async def _subscribe(self) -> None: pair: TradingPair = self.live_strategy_.trading_pair_ for instrument in pair.instruments_: await self.pricer_client_.subscribe(CvttPricesSubscription( exchange_config_name=instrument["exchange_config_name"], instrument_id=instrument["instrument_id"], interval_sec=60, history_depth_sec=60*60*24, callback=partial(self.on_message, instrument_id=instrument["instrument_id"]) )) async def on_message(self, message_type: MessageTypeT, subscr_id: SubscriptionIdT, message: Dict, instrument_id: str) -> None: Log.info(f"{self.fname()}: {message_type=} {subscr_id=} {instrument_id}") aggr: JsonDictT if message_type == "md_aggregate": aggr = message.get("md_aggregate", {}) await self.live_strategy_.on_mkt_data_update(aggr) # print(f"[{aggr['tstmp'][:19]}] *** RLTM *** {message}") elif message_type == "historical_md_aggregate": aggr = message.get("historical_data", {}) await self.live_strategy_.on_mkt_data_hist_snapshot(aggr) # print(f"[{aggr['tstmp'][:19]}] *** HIST *** {aggr}") else: Log.info(f"Unknown message type: {message_type}") async def run(self) -> None: await self.pricer_client_.run() class PtLiveStrategy(NamedObject): config_: Dict[str, Any] trading_pair_: TradingPair model_data_policy_: ModelDataPolicy pt_mkt_data_: RealTimeMarketData pt_mkt_data_client_: PtMktDataClient # for presentation: history of prediction values and trading signals predictions_: pd.DataFrame trading_signals_: pd.DataFrame def __init__( self, config: Dict[str, Any], instruments: List[Dict[str, str]], ): self.config_ = config self.trading_pair_ = TradingPair(config=config, instruments=instruments) self.predictions_ = pd.DataFrame() self.trading_signals_ = pd.DataFrame() import copy # modified config must be passed to PtMarketData config_copy = copy.deepcopy(config) config_copy["instruments"] = instruments self.pt_mkt_data_ = RealTimeMarketData(config=config_copy) self.model_data_policy_ = ModelDataPolicy.create( config, is_real_time=True,pair=self.trading_pair_ ) async def on_mkt_data_hist_snapshot(self, aggr: JsonDictT) -> None: Log.info(f"on_mkt_data_hist_snapshot: {aggr}") await self.pt_mkt_data_.on_mkt_data_hist_snapshot(snapshot=aggr) pass async def on_mkt_data_update(self, aggr: JsonDictT) -> None: market_data_df = await self.pt_mkt_data_.on_mkt_data_update(update=aggr) if market_data_df is not None: self.trading_pair_.market_data_ = market_data_df self.model_data_policy_.advance() prediction = self.trading_pair_.run(market_data_df, self.model_data_policy_.advance()) self.predictions_ = pd.concat([self.predictions_, prediction.to_df()], ignore_index=True) trades = self._create_trades(prediction=prediction, last_row=market_data_df.iloc[-1]) # URGENT implement this pass async def run(self) -> None: await self.pt_mkt_data_client_.run() 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