progress: stop signals
This commit is contained in:
Oleg Sheynin
parent
ca9fff8d88
commit
c776c95d69
@ -45,7 +45,6 @@ Each configuration dictionary specifies:
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- `instruments`: A list of symbols to consider for forming trading pairs.
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- `trading_hours`: Defines the session start and end times, crucial for equity markets.
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- `price_column`: The column in the data to be used as the price (e.g., "close").
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- `zero_threshold`: A small value to handle potential division by zero.
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- `dis-equilibrium_open_trshld`: The threshold (in standard deviations) of the dis-equilibrium for opening a trade.
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- `dis-equilibrium_close_trshld`: The threshold (in standard deviations) of the dis-equilibrium for closing an open trade.
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- `training_minutes`: The length of the rolling window (in minutes) used to train the model (e.g., calculate cointegration, mean, and standard deviation of the dis-equilibrium).
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@ -7,24 +7,27 @@
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"db_table_name": "md_1min_bars",
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"exchange_id": "BNBSPOT",
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"instrument_id_pfx": "PAIR-",
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"trading_hours": {
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"begin_session": "00:00:00",
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"end_session": "23:59:00",
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"timezone": "UTC"
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},
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"price_column": "close",
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"zero_threshold": 1e-10,
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"dis-equilibrium_open_trshld": 2.0,
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"dis-equilibrium_close_trshld": 0.5,
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"training_minutes": 120,
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"funding_per_pair": 2000.0,
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# ====== Trading Parameters ======
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"price_column": "close",
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"dis-equilibrium_open_trshld": 2.0,
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"dis-equilibrium_close_trshld": 1.0,
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"training_minutes": 120,
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"fit_method_class": "pt_trading.sliding_fit.SlidingFit",
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# "fit_method_class": "pt_trading.static_fit.StaticFit",
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"close_outstanding_positions": true,
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"trading_hours": {
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"begin_session": "06:00:00",
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"end_session": "16:00:00",
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"timezone": "America/New_York"
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# ====== Stop Conditions ======
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"stop_close_conditions": {
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"profit": 2.0,
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"loss": -0.5
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}
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# ====== End of Session Closeout ======
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"close_outstanding_positions": true,
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# "close_outstanding_positions": false,
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"trading_hours": {
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"begin_session": "9:30:00",
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"end_session": "21:30:00",
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"timezone": "America/New_York"
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}
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}
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@ -7,24 +7,20 @@
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"db_table_name": "md_1min_bars",
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"exchange_id": "ALPACA",
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"instrument_id_pfx": "STOCK-",
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"trading_hours": {
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"begin_session": "9:30:00",
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"end_session": "16:00:00",
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"timezone": "America/New_York"
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},
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"price_column": "close",
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"funding_per_pair": 2000.0,
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"zero_threshold": 1e-10,
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#
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"dis-equilibrium_open_trshld": 2.0,
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"dis-equilibrium_close_trshld": 1.0,
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"training_minutes": 150,
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"fit_method_class": "pt_trading.sliding_fit.SlidingFit",
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"exclude_instruments": ["CAN"],
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"funding_per_pair": 2000.0,
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# ====== Trading Parameters ======
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"price_column": "close",
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"dis-equilibrium_open_trshld": 2.0,
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"dis-equilibrium_close_trshld": 1.0,
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"training_minutes": 120,
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"fit_method_class": "pt_trading.sliding_fit.SlidingFit",
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# ====== Stop Conditions ======
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"stop_conditions": {
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"profit": 1.0,
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"stop_close_conditions": {
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"profit": 2.0,
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"loss": -0.5
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}
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@ -1,26 +0,0 @@
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{
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"security_type": "EQUITY",
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"data_directory": "./data/equity",
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# "datafiles": [
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# "20250604.mktdata.ohlcv.db",
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# ],
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"db_table_name": "md_1min_bars",
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"exchange_id": "ALPACA",
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"instrument_id_pfx": "STOCK-",
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"trading_hours": {
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"begin_session": "9:30:00",
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"end_session": "16:00:00",
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"timezone": "America/New_York"
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},
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"price_column": "close",
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"zero_threshold": 1e-10,
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"dis-equilibrium_open_trshld": 2.0,
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"dis-equilibrium_close_trshld": 1.0,
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"training_minutes": 120,
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"funding_per_pair": 2000.0,
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"fit_method_class": "pt_trading.sliding_fit.SlidingFit",
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# "fit_method_class": "pt_trading.static_fit.StaticFit",
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"exclude_instruments": ["CAN"],
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"close_outstanding_positions": false
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}
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@ -29,8 +29,3 @@ class PairsTradingFitMethod(ABC):
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def reset(self) -> None: ...
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class PairState(Enum):
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INITIAL = 1
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OPEN = 2
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CLOSED = 3
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CLOSED_POSITIONS = 4
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@ -3,9 +3,9 @@ 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.fit_method import PairsTradingFitMethod
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from pt_trading.results import BacktestResult
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from pt_trading.trading_pair import CointegrationData, TradingPair
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from pt_trading.trading_pair import CointegrationData, TradingPair, PairState
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NanoPerMin = 1e9
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@ -72,7 +72,7 @@ class SlidingFit(PairsTradingFitMethod):
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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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print(f"***{pair}*** FINISHED *** Num Trades:{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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@ -86,24 +86,41 @@ class SlidingFit(PairsTradingFitMethod):
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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, PairState.CLOSED_POSITIONS]:
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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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scaled_disequilibrium = pred_row["scaled_disequilibrium"]
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if pair.user_data_["state"] in [PairState.INITIAL, PairState.CLOSE, PairState.CLOSE_POSITION]:
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if scaled_disequilibrium >= open_threshold:
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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"] = PairState.OPEN.name
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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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pair.on_open_trades(open_trades)
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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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if scaled_disequilibrium <= close_threshold:
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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"] = PairState.CLOSE.name
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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.CLOSE
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pair.on_close_trades(close_trades)
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elif pair.to_stop_close_conditions(predicted_row=pred_row):
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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"] = pair.user_data_["stop_close_state"].name
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print(f"STOP CLOSE TRADES:\n{close_trades}")
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pair.add_trades(close_trades)
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pair.user_data_["state"] = pair.user_data_["stop_close_state"]
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pair.on_close_trades(close_trades)
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# Outstanding positions
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if pair.user_data_["state"] == PairState.OPEN:
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@ -112,16 +129,17 @@ class SlidingFit(PairsTradingFitMethod):
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)
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# outstanding positions
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if config["close_outstanding_positions"]:
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close_position_trades = self._get_close_position_trades(
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close_position_trades = self._get_close_trades(
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pair=pair,
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row=pred_row,
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close_threshold=close_threshold,
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)
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if close_position_trades is not None:
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close_position_trades["status"] = "CLOSE_POSITION"
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close_position_trades["status"] = PairState.CLOSE_POSITION.name
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print(f"CLOSE_POSITION TRADES:\n{close_position_trades}")
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pair.add_trades(close_position_trades)
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pair.user_data_["state"] = PairState.CLOSED_POSITIONS
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pair.user_data_["state"] = PairState.CLOSE_POSITION
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pair.on_close_trades(close_position_trades)
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else:
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if pair.predicted_df_ is not None:
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bt_result.handle_outstanding_position(
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@ -154,9 +172,6 @@ class SlidingFit(PairsTradingFitMethod):
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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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@ -237,8 +252,6 @@ class SlidingFit(PairsTradingFitMethod):
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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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@ -276,61 +289,114 @@ class SlidingFit(PairsTradingFitMethod):
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"pair": "object"
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})
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def _get_close_position_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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# def _get_stop_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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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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# stop_close_row = row
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# stop_close_tstamp = stop_close_row["tstamp"]
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# stop_close_disequilibrium = stop_close_row["disequilibrium"]
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# stop_close_scaled_disequilibrium = stop_close_row["scaled_disequilibrium"]
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# stop_close_px_a = stop_close_row[f"{colname_a}"]
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# stop_close_px_b = stop_close_row[f"{colname_b}"]
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close_position_row = row
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close_position_tstamp = close_position_row["tstamp"]
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close_position_disequilibrium = close_position_row["disequilibrium"]
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close_position_scaled_disequilibrium = close_position_row["scaled_disequilibrium"]
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close_position_px_a = close_position_row[f"{colname_a}"]
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close_position_px_b = close_position_row[f"{colname_b}"]
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# stop_close_side_a = pair.user_data_["close_side_a"]
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# stop_close_side_b = pair.user_data_["close_side_b"]
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close_position_side_a = pair.user_data_["close_side_a"]
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close_position_side_b = pair.user_data_["close_side_b"]
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# trd_signal_tuples = [
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# (
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# stop_close_tstamp,
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# stop_close_side_a,
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# pair.symbol_a_,
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# stop_close_px_a,
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# stop_close_disequilibrium,
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# stop_close_scaled_disequilibrium,
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# pair,
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# ),
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# (
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# stop_close_tstamp,
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# stop_close_side_b,
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# pair.symbol_b_,
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# stop_close_px_b,
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# stop_close_disequilibrium,
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# stop_close_scaled_disequilibrium,
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# pair,
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# ),
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# ]
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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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trd_signal_tuples = [
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(
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close_position_tstamp,
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close_position_side_a,
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pair.symbol_a_,
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close_position_px_a,
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close_position_disequilibrium,
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close_position_scaled_disequilibrium,
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pair,
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),
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(
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close_position_tstamp,
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close_position_side_b,
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pair.symbol_b_,
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close_position_px_b,
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close_position_disequilibrium,
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close_position_scaled_disequilibrium,
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pair,
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),
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]
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# def _get_close_position_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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# 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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# 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_position_row = row
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# close_position_tstamp = close_position_row["tstamp"]
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# close_position_disequilibrium = close_position_row["disequilibrium"]
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# close_position_scaled_disequilibrium = close_position_row["scaled_disequilibrium"]
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# close_position_px_a = close_position_row[f"{colname_a}"]
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# close_position_px_b = close_position_row[f"{colname_b}"]
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# close_position_side_a = pair.user_data_["close_side_a"]
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# close_position_side_b = pair.user_data_["close_side_b"]
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# trd_signal_tuples = [
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# (
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# close_position_tstamp,
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# close_position_side_a,
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# pair.symbol_a_,
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# close_position_px_a,
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# close_position_disequilibrium,
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# close_position_scaled_disequilibrium,
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# pair,
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# ),
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# (
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# close_position_tstamp,
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# close_position_side_b,
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# pair.symbol_b_,
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# close_position_px_b,
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# close_position_disequilibrium,
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# close_position_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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curr_training_start_idx = 0
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@ -1,9 +1,18 @@
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from __future__ import annotations
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from enum import Enum
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from typing import Any, Dict, List, Optional
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import pandas as pd # type:ignore
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from statsmodels.tsa.vector_ar.vecm import VECM, VECMResults # type:ignore
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from statsmodels.tsa.vector_ar.vecm import VECM, VECMResults
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class PairState(Enum):
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INITIAL = 1
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OPEN = 2
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CLOSE = 3
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||||
CLOSE_POSITION = 4
|
||||
CLOSE_STOP_LOSS = 5
|
||||
CLOSE_STOP_PROFIT = 6
|
||||
|
||||
class CointegrationData:
|
||||
EG_PVALUE_THRESHOLD = 0.05
|
||||
@ -288,13 +297,6 @@ class TradingPair:
|
||||
/ self.training_std_
|
||||
)
|
||||
|
||||
# print("*** PREDICTED DF")
|
||||
# print(predicted_df)
|
||||
# print("*" * 80)
|
||||
# print("*** SELF.PREDICTED_DF")
|
||||
# print(self.predicted_df_)
|
||||
# print("*" * 80)
|
||||
|
||||
predicted_df = predicted_df.reset_index(drop=True)
|
||||
if self.predicted_df_ is None:
|
||||
self.predicted_df_ = predicted_df
|
||||
@ -343,6 +345,49 @@ class TradingPair:
|
||||
curr_training_start_idx += 1
|
||||
return result
|
||||
|
||||
def to_stop_close_conditions(self, predicted_row: pd.Series) -> bool:
|
||||
config = self.config_
|
||||
if ("stop_close_conditions" not in config or config["stop_close_conditions"] is None) :
|
||||
return False
|
||||
if "profit" in config["stop_close_conditions"]:
|
||||
current_return = self._current_return(predicted_row)
|
||||
#
|
||||
# print(f"time={predicted_row['tstamp']} current_return={current_return}")
|
||||
#
|
||||
if current_return >= config["stop_close_conditions"]["profit"]:
|
||||
self.user_data_["stop_close_state"] = PairState.CLOSE_STOP_PROFIT
|
||||
return True
|
||||
if "loss" in config["stop_close_conditions"]:
|
||||
if current_return <= config["stop_close_conditions"]["loss"]:
|
||||
self.user_data_["stop_close_state"] = PairState.CLOSE_STOP_LOSS
|
||||
return True
|
||||
return False
|
||||
|
||||
def on_open_trades(self, trades: pd.DataFrame) -> None:
|
||||
if "close_trades" in self.user_data_: del self.user_data_["close_trades"]
|
||||
self.user_data_["open_trades"] = trades
|
||||
|
||||
def on_close_trades(self, trades: pd.DataFrame) -> None:
|
||||
del self.user_data_["open_trades"]
|
||||
self.user_data_["close_trades"] = trades
|
||||
|
||||
def _current_return(self, predicted_row: pd.Series) -> float:
|
||||
if "open_trades" in self.user_data_:
|
||||
open_trades = self.user_data_["open_trades"]
|
||||
if len(open_trades) == 0:
|
||||
return 0.0
|
||||
def _stock_return(stock: str) -> float:
|
||||
stock_open_trades = open_trades[open_trades["symbol"] == stock]
|
||||
stock_sign = -1 if stock_open_trades["action"].iloc[0] == "SELL" else 1
|
||||
stock_price = predicted_row[f"{self.price_column_}_{stock}"]
|
||||
stock_return = stock_sign * (stock_price - stock_open_trades["price"].iloc[0]) / stock_open_trades["price"].iloc[0]
|
||||
return float(stock_return)
|
||||
|
||||
stock_a_return = _stock_return(self.symbol_a_)
|
||||
stock_b_return = _stock_return(self.symbol_b_)
|
||||
return (stock_a_return + stock_b_return) * 100.0
|
||||
return 0.0
|
||||
|
||||
def __repr__(self) -> str:
|
||||
return self.name()
|
||||
|
||||
|
||||
File diff suppressed because one or more lines are too long
Loading…
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Reference in New Issue
Block a user