From 90ddbbfccca0f18ba5ee527998ec504baacef330 Mon Sep 17 00:00:00 2001 From: Blair Azzopardi Date: Mon, 6 May 2024 18:41:29 +0100 Subject: [PATCH] Add run_batch method. Add sharpe ration as internal calc. Allow weiner path sims to have different sigmas. Swap ql for pyfeng in notebooks. Add some features to README. --- README.md | 14 +- build.py => build_mypyc.py | 0 poetry.lock | 1368 +++++++++++++------------- pyproject.toml | 5 +- yabte/__init__.py | 5 +- yabte/backtest/strategyrunner.py | 15 +- yabte/tests/test_strategy_runner.py | 29 + yabte/utilities/pandas_extension.py | 14 +- yabte/utilities/simulation/weiner.py | 6 +- 9 files changed, 777 insertions(+), 679 deletions(-) rename build.py => build_mypyc.py (100%) diff --git a/README.md b/README.md index 430e450..ec28240 100644 --- a/README.md +++ b/README.md @@ -2,11 +2,17 @@ Python module for backtesting trading strategies. -Support event driven backtesting, ie `on_open`, `on_close`, etc. Also supports multiple assets. +Features -Very basic statistics like book cash, mtm and total value. Currently, everything else needs to be deferred to a 3rd party module like `empyrical`. +* Event driven, ie `on_open`, `on_close`, etc. +* Multiple assets. +* OHLC Asset. Extendable (e.g support additional fields, e.g. Volatility, or entirely different fields, e.g. Barrels per day). +* Multiple books. +* Positional and Basket orders. Extendible (e.g. can support stop loss). +* Batch runs (for optimization). +* Captures book history including transactions & daily cash, MtM and total values. -There are some basic tests but use at your own peril. It's not production level code. +The module provides basic statistics like book cash, mtm and total value. Currently, everything else needs to be deferred to a 3rd party module like `empyrical`. ## Core dependencies @@ -20,7 +26,7 @@ pip install yatbe ## Usage -Below is an example usage (the performance of the example strategy won't be good). +Below is an example usage (the economic performance of the example strategy won't be good). ```python import pandas as pd diff --git a/build.py b/build_mypyc.py similarity index 100% rename from build.py rename to build_mypyc.py diff --git a/poetry.lock b/poetry.lock index d8f5edf..39e5d6d 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. 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ipykernel = "^6.20.2" pyfeng = "^0.2.5" nbconvert = "^7.2.9" +quantlib = "^1.34" [tool.isort] profile = "black" diff --git a/yabte/__init__.py b/yabte/__init__.py index 9c2de6a..9c68310 100644 --- a/yabte/__init__.py +++ b/yabte/__init__.py @@ -1 +1,4 @@ -_author__ = "Blair Azzopardi" +from importlib.metadata import version + +__author__ = "Blair Azzopardi" +__version__ = version(__package__) diff --git a/yabte/backtest/strategyrunner.py b/yabte/backtest/strategyrunner.py index 314358e..dc193b4 100644 --- a/yabte/backtest/strategyrunner.py +++ b/yabte/backtest/strategyrunner.py @@ -1,7 +1,9 @@ +import concurrent.futures import logging +from concurrent.futures import ProcessPoolExecutor from copy import deepcopy from dataclasses import dataclass, field -from typing import Any, Dict, List, Optional +from typing import Any, Dict, Iterable, List, Optional import pandas as pd @@ -219,3 +221,14 @@ def run(self, params: Dict[str, Any] = None) -> StrategyRunnerResult: book.eod_tasks(ts, day_data, asset_map) return srr + + def run_batch( + self, + params_iterable: Iterable[Dict[str, Any]], + executor: ProcessPoolExecutor | None = None, + ) -> List[StrategyRunnerResult]: + """Run a set of parameter combinations.""" + + executor = executor or concurrent.futures.ThreadPoolExecutor() + with executor: + return list(executor.map(self.run, params_iterable)) diff --git a/yabte/tests/test_strategy_runner.py b/yabte/tests/test_strategy_runner.py index 06b59be..59de2a6 100644 --- a/yabte/tests/test_strategy_runner.py +++ b/yabte/tests/test_strategy_runner.py @@ -5,6 +5,7 @@ import numpy as np import pandas as pd +import yabte.utilities.pandas_extension from yabte.backtest import ( BasketOrder, Book, @@ -571,6 +572,34 @@ def on_close(self): [(o.status, o.key, o.size) for o in srr.orders_unprocessed], ) + def test_run_batch(self): + + book = Book(name="Main", cash=Decimal("100000")) + + sr = StrategyRunner( + data=self.df_combined, + assets=self.assets, + strategies=[TestSMAXOStrat()], + books=[book], + ) + + param_iter = [ + {"days_long": n, "days_short": m} + for n, m in zip([20, 30, 40, 50], [5, 10, 15, 20]) + if n > m + ] + + srrs = sr.run_batch(param_iter) + + self.assertEqual(len(srrs), len(param_iter)) + + # check we have distinct sharpe ratios for each param set + sharpes = { + srr.book_history.loc[:, ("Main", "total")].prc.sharpe_ratio() + for srr in srrs + } + self.assertEqual(len(sharpes), len(param_iter)) + if __name__ == "__main__": unittest.main() diff --git a/yabte/utilities/pandas_extension.py b/yabte/utilities/pandas_extension.py index d07a13b..2998143 100644 --- a/yabte/utilities/pandas_extension.py +++ b/yabte/utilities/pandas_extension.py @@ -13,10 +13,10 @@ def standard(self): @pd.api.extensions.register_dataframe_accessor("prc") +@pd.api.extensions.register_series_accessor("prc") class PriceAccessor: # TODO: add ledoit cov (via sklearn) # http://www.ledoit.net/honey.pdf - # TODO: add Sharpe ratio def __init__(self, pandas_obj): self._validate(pandas_obj) @@ -24,11 +24,12 @@ def __init__(self, pandas_obj): @staticmethod def _validate(obj): - if (obj < 0).any(axis=None): - raise AttributeError("Prices must be non-negative") + pass @property def log_returns(self): + if (self._obj < 0).any(axis=None): + raise AttributeError("Prices must be non-negative for log returns") return np.log((self._obj / self._obj.shift())[1:]) @property @@ -42,6 +43,8 @@ def frequency(self): return 252 elif days == 7: return 52 + elif 28 <= days <= 31: + return 12 def capm_returns(self, risk_free_rate=0): returns = self.returns @@ -56,6 +59,11 @@ def capm_returns(self, risk_free_rate=0): + betas * (returns_mkt.mean() * self.frequency - risk_free_rate) ).rename("CAPM") + def sharpe_ratio(self, risk_free_rate=0, use_log_returns=True): + ann_factor = np.sqrt(self.frequency) + returns = self.log_returns if use_log_returns else self.returns + return ann_factor * (returns.mean() - risk_free_rate) / returns.std() + def null_blips(self, sd=5, sdd=7): df = self._obj z = df.scl.standard diff --git a/yabte/utilities/simulation/weiner.py b/yabte/utilities/simulation/weiner.py index a0e3a09..bb5571d 100644 --- a/yabte/utilities/simulation/weiner.py +++ b/yabte/utilities/simulation/weiner.py @@ -14,7 +14,7 @@ def weiner_simulate_paths( n_steps: int, n_sims: int = 1, - stdev: float = 1, + stdev: float | np.ndarray = 1, R: np.ndarray = np.array([[1]]), rng=None, ): @@ -22,10 +22,12 @@ def weiner_simulate_paths( `stdev` is the increment size, `R` a correlation matrix, `n_steps` is how many time steps, `n_sims` the number of simulations and `rng` - a numpy random number generator (optional). + a numpy random number generator (optional). If `stdev` is a scalar + it will be broadcasted to the size of `n_sims`. """ R = np.atleast_2d(R) + stdev = np.resize(stdev, n_sims).reshape(n_sims, 1) if rng is None: rng = np.random.default_rng()