modular-backtest 0.0.1.post1

Backtesting Framework for Algorithmic Trading

Modular Backtest

About the project

Modular Backtest is backtesting framework for algorithmic trading written in Python, focuses on modularity and resusability. The framework is built on top of zipline-reloaded, a Pythonic algorithmic trading library. It consists of the following modules with according responsibility.

• Asset selection
  select the assets to be included in the universe.

• Signal generation
  generate signal sentiment for the given assets.

• Portfolio construction
  construct portfolio allocations based on the given signals.

• Risk managements
  adjust the portfolio allocations.

• Order execution
  create buy/sell orders responding to the constructed allocations.

• Data
  downlod and ingest equity's historical prices via public API. Currently support Yahoo Finance.

• Factors
  compute statistics and technical indicators used in trading.

Getting started

Installation

pip install modular-backtest

Data ingestion

After installed, add the line from modular_backtest.data.bundles import yahoo to ~/.zipline/extension.py and then run

zipline ingest -b yahoo-finance

Usages

from datetime import datetime
import pandas as pd
from modular_backtest.data.bundles import yahoo
from modular_backtest.engine import BacktestEngine
from modular_backtest.factors.handler import FactorHandler
from modular_backtest.models.asset_selection import ManualAssetSelection
from modular_backtest.models.handler import ModelHandler
from modular_backtest.models.order_execution import InstantOrderExecution
from modular_backtest.models.portfolio_construction import (
    EqualWeightPortfolioConstruction,
)
from modular_backtest.models.risk_management import VoidRiskManagement
from modular_backtest.models.signal_generation import StaticSignalGeneration

models = ModelHandler(
    ManualAssetSelection(["AAPL", "MSFT", "AMZN", "SPY", "QQQ"]),
    StaticSignalGeneration(),
    EqualWeightPortfolioConstruction(),
    InstantOrderExecution(),
    VoidRiskManagement(),
)

engine = BacktestEngine(models=models, factors=FactorHandler())

res = engine.run(
    start=datetime(2024, 1, 1), end=datetime(2024, 12, 31), bundle=yahoo.NAME
)

res.data.portfolio_value
# |                           |   portfolio_value |
# |:--------------------------|------------------:|
# | 2024-01-02 21:00:00+00:00 |          100000   |
# | 2024-01-03 21:00:00+00:00 |           99852.6 |
# | 2024-01-04 21:00:00+00:00 |           98775.5 |
# | 2024-01-05 21:00:00+00:00 |           98826.4 |
# | 2024-01-08 21:00:00+00:00 |          100890   |
# | 2024-01-09 21:00:00+00:00 |          101222   |
# | 2024-01-10 21:00:00+00:00 |          102281   |
# | 2024-01-11 21:00:00+00:00 |          102544   |
# | 2024-01-12 21:00:00+00:00 |          102735   |
# | 2024-01-16 21:00:00+00:00 |          102308   |
# | 2024-01-17 21:00:00+00:00 |          101740   |
# | 2024-01-18 21:00:00+00:00 |          103333   |
# | 2024-01-19 21:00:00+00:00 |          104815   |
# | 2024-01-22 21:00:00+00:00 |          104953   |
# | 2024-01-23 21:00:00+00:00 |          105534   |
# | 2024-01-24 21:00:00+00:00 |          105908   |
# | 2024-01-25 21:00:00+00:00 |          106253   |
# | 2024-01-26 21:00:00+00:00 |          106046   |
# | 2024-01-29 21:00:00+00:00 |          106948   |
# | 2024-01-30 21:00:00+00:00 |          106023   |
# | 2024-01-31 21:00:00+00:00 |          103769   |
# | 2024-02-01 21:00:00+00:00 |          105433   |
# | 2024-02-02 21:00:00+00:00 |          107957   |
# ...
# | 2024-12-26 21:00:00+00:00 |          135358   |
# | 2024-12-27 21:00:00+00:00 |          133499   |
# | 2024-12-30 21:00:00+00:00 |          131844   |
# | 2024-12-31 21:00:00+00:00 |          130903   |

Documentation

License

Distributed under the MIT License. See LICENSE for more information.

Maintainers

modular-backtest is currently maintained by kfuangsung ([e-mail]).

Acknowledgments

• zipline-reloaded: A Pythonic Algorithmic Trading Library (forked from quantopian/zipline)

Disclaimer

Please remember that past performance may not be indicative of future results. Different types of investments involve varying degrees of risk, and there can be no assurance that the future performance of any specific investment, investment strategy, or product made reference to directly or indirectly in this page, will be profitable, equal any corresponding indicated historical performance level(s), or be suitable for your portfolio.

The hypothetical backtested performance does not represent the results of actual trading and does not and is not intended to indicate the past performance or future performance of investment strategy.

The hypothetical backtested performance results for each strategy include estimated values for transaction costs of buying and selling securities, which may not be accurate. Investment management fees (including without limitation management fees and performance fees), custody and other costs, and taxes are not included in performance results.

The hypothetical performance does not reflect the reinvestment of dividends and distributions therefrom, interest, capital gains and withholding taxes.

Simulated returns may be dependent on the market and economic conditions that existed during the period. Future market or economic conditions can adversely affect the returns.