Quantropy Save

The human mind is fascinating. Give it a series of observations, and it will attempt to find structure to it. It will find variables upon which the given data might depend on, and develop elaborate models in the hopes of predicting future observations. What if this search for the Holy Grail is all in vain? What if we have been fooled by randomness?

Table of Contents

• Getting Started
  • Docker
  • Git
• Proof of Concept
• Architectural Design
• Acknowledgment
• Contributing
• Getting in Touch

Getting Started

This project is an attempt on shedding light to this question that has puzzled researchers over the past century. It is the culmination of three years of learning about the financial markets, in order to develop a platform in the hopes of providing a comprehensive and unified approach to trading the financial markets.

To fetch and run the project, you can choose one of the methods below. If successful, Quantropy should now be running here.

Docker

docker pull matilda
docker run -d -p 5000:5000 matilda

Git

git clone https://github.com/AlainDaccache/Quantropy/
cd Quantropy
py -m pip install -r requirements.txt
set FLASK_APP=matilda
py -m flask run

Proof of Concept

This open-source project is built with all types of users in mind. Whether you're a seasoned trader that wants to progressively learn how to code in order to automate your strategies, or vice-versa, the documentation covers both aspects. Through it, we synthesize the theoretical groundwork that was laid by academicians and industry practitioners for conducting fundamental, technical, and quantitative analysis, and using these for stock picking, market timing, and portfolio allocation. Alongside it, we provide an implementation that uses our API calls to apply and validate these models in real-life.

# some imports for minimal example
from matilda import piotroski_f_score, earnings_per_share, return_on_equity, FactorModels, \
    EquallyWeightedPortfolio, AlpacaBroker
from matilda.metrics_helpers import mean_metric_growth_rate, compare_against_macro

# initialize stock screener with an initial universe of Dow Jones stocks
stock_screener = StockScreener(securities_universe=config.MarketIndices.DOW_JONES)

# filter by industry, sector, location, exchange...
stock_screener.filter_by_market(filter=[config.GICS_Sectors.INFORMATION_TECHNOLOGY,
                                        config.GICS_Sectors.CONSUMER_DISCRETIONARY])

# filter by fundamental metric against absolute number.
# The Piotroski score, a criteria-based metric used to evaluate value stocks, should be above 8.
stock_screener.filter_by_comparison_to_number(partial(piotroski_f_score, period='FY'), '>=', 8)

stock_screener.run()  # can run the stock screener, by default based on today's values

# can also filter based on growth, mean, etc. over time.
fn = partial(mean_metric_growth_rate, metric=earnings_per_share, interval='Y-Y', periods=1)
# EPS growth rates of at least 25% compared with year-ago levels suggest a company has products/services in strong demand
stock_screener.filter_by_comparison_to_number(fn, '>=', 0.25)

# can also filter these based on percentile against competitors (industry, sector...)
# Ideally, ROE is equal to or just above the median for the peer group
fn = partial(compare_against_macro, metric=return_on_equity, against=config.SIC_Industries)
stock_screener.filter_by_comparison_to_number(fn, '>=', 50)

# regress against exposure to a certain risk factor model
lower_bounds = pd.Series(data=[40], index=['Alpha'])
upper_bounds = pd.Series(data=[80], index=['MKT'])
stock_screener.filter_by_exposure_from_factor_model(factor_model=FactorModels.FamaFrench3,
                                                    lower_bounds=lower_bounds, upper_bounds=upper_bounds)

# can also specify another date when running the stock screener
stock_screener.run(date=datetime(2018, 1, 1))
print(stock_screener.stocks)


# specify your strategy's rules for stock selection, portfolio allocation, and market timing
class CustomStrategy(Strategy):
    def is_market_timing(self, portfolio):
        # rebalance every quarter (3 months)
        current_date = portfolio.df_returns.index[-1]
        last_rebalancing_day = portfolio.last_rebalancing_day
        return (current_date - last_rebalancing_day).days > config.RebalancingFrequency.Quarterly.value

    def screen_stocks(self, current_date):
        # use the stock screener we previous specified
        stock_screener.run(date=current_date)
        return stock_screener.stocks, []

    def portfolio_allocation_regime(self, portfolio):
        return EquallyWeightedPortfolio(portfolio).solve_weights()


# instantiate the custom strategy
strategy = CustomStrategy(max_stocks_count_in_portfolio=12, net_exposure=(100, 0),
                          maximum_leverage=1.0, reinvest_dividends=True, fractional_shares=True)

# historically simulate (i.e. backtest) your strategy
strategy.historical_simulation(starting_date=datetime(2019, 1, 1), ending_date=datetime(2020, 12, 1),
                               starting_capital=50000, include_capital_gains_tax=False,
                               include_slippage=False)

# deploy your strategy. fill your broker's API key ID and secret in config.py
strategy.broker_deployment(broker=AlpacaBroker())

Architectural Design

Essentially, we standardize algorithmic trading by decoupling analytics, data providers, and brokers, to allow the user to flexibly and comprehensively research models, develop strategies, and deploy them in real-time. The flow looks as such:

The library attempts to:

• Implement the low-level work to achieve abstraction, so that the user can swiftly translate his insights into practice, without wasting time, energy, and money to integrate existing solutions or reinvent the wheel if not possible.
• Follow good design practices, from both object-oriented and functional programming paradigms to achieve modularity, allowing the user to swap in their components while still being able to reuse and extend on our framework.
• Make use of a DevOps pipeline to achieve continuous integration and delivery, integrating a stack of cutting-edge technologies.

Acknowledgment

Thanks to part of McGill University's generous donation, I was able to acquire these books that I will use as reference throughout the implementation of this project:

• Andrew Ang. Asset Management: A Systematic Approach to Factor Investing
• Ernie Chan - Algorithmic Trading: Winning Strategies and Their Rationale
• Ernie Chan - Quantitative Trading: How to Build Your Own Algorithmic Trading Business
• Stefan Jansen - Machine Learning for Algorithmic Trading
• Marcos Lopez de Prado - Advances in Financial Machine Learning
• Marcos Lopez de Prado - Machine Learning for Asset Managers
• Edward Qian - Quantitative Equity Portfolio Management: Modern Techniques and Applications

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change. Here is a list of areas that I think Quantropy can really benefit from:

• Data Scraping: Scrape alternative data (news sentiment analysis, web/app usage and reviews etc.), improve the HTML scraper for Edgar.
• Factor Library: Use our risk factor modeling interface to develop and publish your own factors! We can surely integrate them to develop our own community's asset pricing model (and perhaps our fund :smirk:).
• Data Visualization: Develop more visualizations using Bokeh. Integrate with PowerBI.
• Portfolio management: Implementing pre-defined strategies of fund managers (i.e. Warren Buffet, Benjamin Graham, Peter Lynch) based on books written and interviews. Extend broker deployment implementation.
• Misc: And of course, we can never get enough of unit tests and documentation!

Getting in Touch

If you are having a problem with Quantropy, please raise a GitHub issue. For anything else, you can reach me at: