progress
This commit is contained in:
Oleg Sheynin
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from __future__ import annotations
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from functools import partial
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from typing import Any, Dict, List, Optional
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import pandas as pd
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from cvttpy_base.settings.cvtt_types import JsonDictT
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from cvttpy_base.tools.base import NamedObject
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from cvttpy_base.tools.logger import Log
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from cvtt_client.mkt_data import CvttPricerWebSockClient, CvttPricesSubscription, MessageTypeT, SubscriptionIdT
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from pt_strategy.model_data_policy import ModelDataPolicy
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from pt_strategy.pt_market_data import PtMarketData, RealTimeMarketData
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from pt_strategy.pt_model import Prediction
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from pt_strategy.trading_pair import PairState, TradingPair
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'''
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--config=pair.cfg
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--pair=PAIR-BTC-USDT:COINBASE_AT,PAIR-ETH-USDT:COINBASE_AT
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'''
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class PtMktDataClient(NamedObject):
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live_strategy_: PtLiveStrategy
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pricer_client_: CvttPricerWebSockClient
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subscriptions_: List[CvttPricesSubscription]
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def __init__(self, live_strategy: PtLiveStrategy):
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self.live_strategy_ = live_strategy
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async def start(self, subscription: CvttPricesSubscription) -> None:
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pricer_url = self.live_strategy_.config_.get("pricer_url", None) #, "ws://localhost:12346/ws")
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assert pricer_url is not None, "pricer_url is not found in config"
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self.pricer_client_ = CvttPricerWebSockClient(url=pricer_url)
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await self._subscribe()
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async def _subscribe(self) -> None:
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pair: TradingPair = self.live_strategy_.trading_pair_
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for instrument in pair.instruments_:
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await self.pricer_client_.subscribe(CvttPricesSubscription(
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exchange_config_name=instrument["exchange_config_name"],
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instrument_id=instrument["instrument_id"],
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interval_sec=60,
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history_depth_sec=60*60*24,
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callback=partial(self.on_message, instrument_id=instrument["instrument_id"])
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))
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async def on_message(self, message_type: MessageTypeT, subscr_id: SubscriptionIdT, message: Dict, instrument_id: str) -> None:
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Log.info(f"{self.fname()}: {message_type=} {subscr_id=} {instrument_id}")
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aggr: JsonDictT
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if message_type == "md_aggregate":
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aggr = message.get("md_aggregate", {})
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await self.live_strategy_.on_mkt_data_update(aggr)
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# print(f"[{aggr['tstmp'][:19]}] *** RLTM *** {message}")
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elif message_type == "historical_md_aggregate":
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aggr = message.get("historical_data", {})
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await self.live_strategy_.on_mkt_data_hist_snapshot(aggr)
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# print(f"[{aggr['tstmp'][:19]}] *** HIST *** {aggr}")
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else:
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Log.info(f"Unknown message type: {message_type}")
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async def run(self) -> None:
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await self.pricer_client_.run()
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class PtLiveStrategy(NamedObject):
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config_: Dict[str, Any]
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trading_pair_: TradingPair
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model_data_policy_: ModelDataPolicy
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pt_mkt_data_: RealTimeMarketData
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pt_mkt_data_client_: PtMktDataClient
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# for presentation: history of prediction values and trading signals
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predictions_: pd.DataFrame
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trading_signals_: pd.DataFrame
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def __init__(
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self,
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config: Dict[str, Any],
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instruments: List[Dict[str, str]],
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):
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self.config_ = config
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self.trading_pair_ = TradingPair(config=config, instruments=instruments)
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self.predictions_ = pd.DataFrame()
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self.trading_signals_ = pd.DataFrame()
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import copy
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# modified config must be passed to PtMarketData
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config_copy = copy.deepcopy(config)
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config_copy["instruments"] = instruments
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self.pt_mkt_data_ = RealTimeMarketData(config=config_copy)
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self.model_data_policy_ = ModelDataPolicy.create(
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config, is_real_time=True,pair=self.trading_pair_
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)
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async def on_mkt_data_hist_snapshot(self, aggr: JsonDictT) -> None:
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Log.info(f"on_mkt_data_hist_snapshot: {aggr}")
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await self.pt_mkt_data_.on_mkt_data_hist_snapshot(snapshot=aggr)
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pass
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async def on_mkt_data_update(self, aggr: JsonDictT) -> None:
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market_data_df = await self.pt_mkt_data_.on_mkt_data_update(update=aggr)
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if market_data_df is not None:
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self.trading_pair_.market_data_ = market_data_df
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self.model_data_policy_.advance()
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prediction = self.trading_pair_.run(market_data_df, self.model_data_policy_.advance())
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self.predictions_ = pd.concat([self.predictions_, prediction.to_df()], ignore_index=True)
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trades = self._create_trades(prediction=prediction, last_row=market_data_df.iloc[-1])
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# URGENT implement this
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pass
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async def run(self) -> None:
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await self.pt_mkt_data_client_.run()
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def _create_trades(
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self, prediction: Prediction, last_row: pd.Series
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) -> Optional[pd.DataFrame]:
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pair = self.trading_pair_
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trades = None
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open_threshold = self.config_["dis-equilibrium_open_trshld"]
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close_threshold = self.config_["dis-equilibrium_close_trshld"]
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scaled_disequilibrium = prediction.scaled_disequilibrium_
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abs_scaled_disequilibrium = abs(scaled_disequilibrium)
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if pair.user_data_["state"] in [
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PairState.INITIAL,
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PairState.CLOSE,
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PairState.CLOSE_POSITION,
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PairState.CLOSE_STOP_LOSS,
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PairState.CLOSE_STOP_PROFIT,
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]:
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if abs_scaled_disequilibrium >= open_threshold:
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trades = self._create_open_trades(
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pair, row=last_row, prediction=prediction
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)
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if trades is not None:
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trades["status"] = PairState.OPEN.name
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print(f"OPEN TRADES:\n{trades}")
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pair.user_data_["state"] = PairState.OPEN
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pair.on_open_trades(trades)
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elif pair.user_data_["state"] == PairState.OPEN:
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if abs_scaled_disequilibrium <= close_threshold:
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trades = self._create_close_trades(
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pair, row=last_row, prediction=prediction
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)
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if trades is not None:
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trades["status"] = PairState.CLOSE.name
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print(f"CLOSE TRADES:\n{trades}")
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pair.user_data_["state"] = PairState.CLOSE
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pair.on_close_trades(trades)
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elif pair.to_stop_close_conditions(predicted_row=last_row):
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trades = self._create_close_trades(pair, row=last_row)
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if trades is not None:
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trades["status"] = pair.user_data_["stop_close_state"].name
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print(f"STOP CLOSE TRADES:\n{trades}")
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pair.user_data_["state"] = pair.user_data_["stop_close_state"]
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pair.on_close_trades(trades)
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return trades
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def _handle_outstanding_positions(self) -> Optional[pd.DataFrame]:
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trades = None
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pair = self.trading_pair_
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# Outstanding positions
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if pair.user_data_["state"] == PairState.OPEN:
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print(f"{pair}: *** Position is NOT CLOSED. ***")
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# outstanding positions
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if self.config_["close_outstanding_positions"]:
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close_position_row = pd.Series(pair.market_data_.iloc[-2])
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# close_position_row["disequilibrium"] = 0.0
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# close_position_row["scaled_disequilibrium"] = 0.0
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# close_position_row["signed_scaled_disequilibrium"] = 0.0
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trades = self._create_close_trades(
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pair=pair, row=close_position_row, prediction=None
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)
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if trades is not None:
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trades["status"] = PairState.CLOSE_POSITION.name
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print(f"CLOSE_POSITION TRADES:\n{trades}")
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pair.user_data_["state"] = PairState.CLOSE_POSITION
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pair.on_close_trades(trades)
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else:
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pair.add_outstanding_position(
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symbol=pair.symbol_a_,
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open_side=pair.user_data_["open_side_a"],
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open_px=pair.user_data_["open_px_a"],
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open_tstamp=pair.user_data_["open_tstamp"],
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last_mkt_data_row=pair.market_data_.iloc[-1],
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)
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pair.add_outstanding_position(
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symbol=pair.symbol_b_,
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open_side=pair.user_data_["open_side_b"],
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open_px=pair.user_data_["open_px_b"],
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open_tstamp=pair.user_data_["open_tstamp"],
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last_mkt_data_row=pair.market_data_.iloc[-1],
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)
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return trades
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def _trades_df(self) -> pd.DataFrame:
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types = {
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"time": "datetime64[ns]",
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"action": "string",
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"symbol": "string",
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"side": "string",
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"price": "float64",
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"disequilibrium": "float64",
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"scaled_disequilibrium": "float64",
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"signed_scaled_disequilibrium": "float64",
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# "pair": "object",
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}
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columns = list(types.keys())
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return pd.DataFrame(columns=columns).astype(types)
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def _create_open_trades(
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self, pair: TradingPair, row: pd.Series, prediction: Prediction
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) -> Optional[pd.DataFrame]:
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colname_a, colname_b = pair.exec_prices_colnames()
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tstamp = row["tstamp"]
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diseqlbrm = prediction.disequilibrium_
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scaled_disequilibrium = prediction.scaled_disequilibrium_
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px_a = row[f"{colname_a}"]
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px_b = row[f"{colname_b}"]
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# creating the trades
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df = self._trades_df()
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print(f"OPEN_TRADES: {row["tstamp"]} {scaled_disequilibrium=}")
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if diseqlbrm > 0:
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side_a = "SELL"
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side_b = "BUY"
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else:
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side_a = "BUY"
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side_b = "SELL"
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# save closing sides
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pair.user_data_["open_side_a"] = side_a # used in oustanding positions
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pair.user_data_["open_side_b"] = side_b
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pair.user_data_["open_px_a"] = px_a
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pair.user_data_["open_px_b"] = px_b
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pair.user_data_["open_tstamp"] = tstamp
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pair.user_data_["close_side_a"] = side_b # used for closing trades
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pair.user_data_["close_side_b"] = side_a
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# create opening trades
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df.loc[len(df)] = {
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"time": tstamp,
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"symbol": pair.symbol_a_,
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"side": side_a,
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"action": "OPEN",
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"price": px_a,
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"disequilibrium": diseqlbrm,
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"signed_scaled_disequilibrium": scaled_disequilibrium,
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"scaled_disequilibrium": abs(scaled_disequilibrium),
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# "pair": pair,
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}
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df.loc[len(df)] = {
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"time": tstamp,
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"symbol": pair.symbol_b_,
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"side": side_b,
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"action": "OPEN",
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"price": px_b,
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"disequilibrium": diseqlbrm,
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"scaled_disequilibrium": abs(scaled_disequilibrium),
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"signed_scaled_disequilibrium": scaled_disequilibrium,
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# "pair": pair,
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}
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return df
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def _create_close_trades(
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self, pair: TradingPair, row: pd.Series, prediction: Optional[Prediction] = None
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) -> Optional[pd.DataFrame]:
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colname_a, colname_b = pair.exec_prices_colnames()
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tstamp = row["tstamp"]
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if prediction is not None:
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diseqlbrm = prediction.disequilibrium_
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signed_scaled_disequilibrium = prediction.scaled_disequilibrium_
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scaled_disequilibrium = abs(prediction.scaled_disequilibrium_)
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else:
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diseqlbrm = 0.0
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signed_scaled_disequilibrium = 0.0
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scaled_disequilibrium = 0.0
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px_a = row[f"{colname_a}"]
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px_b = row[f"{colname_b}"]
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# creating the trades
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df = self._trades_df()
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# create opening trades
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df.loc[len(df)] = {
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"time": tstamp,
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"symbol": pair.symbol_a_,
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"side": pair.user_data_["close_side_a"],
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"action": "CLOSE",
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"price": px_a,
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"disequilibrium": diseqlbrm,
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"scaled_disequilibrium": scaled_disequilibrium,
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"signed_scaled_disequilibrium": signed_scaled_disequilibrium,
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# "pair": pair,
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}
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df.loc[len(df)] = {
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"time": tstamp,
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"symbol": pair.symbol_b_,
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"side": pair.user_data_["close_side_b"],
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"action": "CLOSE",
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"price": px_b,
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"disequilibrium": diseqlbrm,
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"scaled_disequilibrium": scaled_disequilibrium,
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"signed_scaled_disequilibrium": signed_scaled_disequilibrium,
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# "pair": pair,
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}
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del pair.user_data_["close_side_a"]
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del pair.user_data_["close_side_b"]
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del pair.user_data_["open_tstamp"]
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del pair.user_data_["open_px_a"]
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del pair.user_data_["open_px_b"]
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del pair.user_data_["open_side_a"]
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del pair.user_data_["open_side_b"]
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return df
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