QRDE-SAT - Quant Research & Development Environment for Systematic Asset Trading

QRDE-SAT houses my personal Python library for Financial Quantitative Research & Development of Trading Strategies.

QRD-SAT provides modular components for data extraction, research, modeling, strategy creation, backtesting, and deployment.

Table of Contents

1. Installation
2. Quant-RDE-SAT Features
   • 2.1) Data Sourcing API
   • 2.2) Research Module
   • 2.3) Quantitative Models Module
   • 2.4) Strategies Module
   • 2.5) Backtesting Module
   • 2.6) Deployment Module
3. Future Works
4. Source Libraries

1) Installation

To install QRDE-SAT, you can try one of the following options:

1. Installation for Users (via pip)

Install QRDE-SAT package to your project virtual environment. To do this simply:

1. Open your project

2. Go to the terminal and run the following (within your virtual environment of choice):

   pip install -e git+https://github.com/alvarosf07/Quant-RDE-SAT.git

2. Installation for Developers

Download QRSDE-SAT in order to work with the source code.

1. Clone the repository:

   git clone https://github.com/alvarosf07/Quant-RDE-SAT

2. Navigate to the main directory of the repository:

   cd ../../Quant-RDE-SAT

3. Install required dependencies (within your local environment):

   pip install -r requirements.txt

2) QRDE-SAT Features

2.1) Data Sourcing API

• Purpose: Seamlessly extract diverse financial data from multiple APIs.

• Key Capabilities:

  • Support for equities, fixed income, commodities, forex, and crypto data.
  • Integration with popular financial APIs:
    • Alpha Vantage
    • Interactive Brokers
    • Yahoo Finance (TBI)
  • Customizable data pipelines for preprocessing and storage.
  
  

2.2) Research Module

• Purpose: Facilitate quantitative research through robust asset and portfolio definitions.

• Key Capabilities:

  • Define and manipulate asset classes (e.g., stocks, bonds, derivatives, currencies, commodities, crypto...).
  • Create portfolio structures for multi-asset analysis.
  • Enable research studies such as risk profiling, factor analysis, and scenario modeling.

  Quick Example:

    from qrde_sat.qrde1_data import *
    from qrde_sat.qrde2_research import *
  
    GS = EquityAsset('GS')
    dataGS = GS.get_ohlcv_data(sd="2024-01-01", ed="2024-11-20", frequency_interval="weekly", api_source="AlphaVantageAPI", data_adjusted="false", extended_hours="false")

  Output:

  Sourcing data from API: --> https://www.alphavantage.co/query?&function=TIME_SERIES_WEEKLY&symbol=MS&outputsize=full&adjusted=false&extended_hours=false&datatype=json&apikey=XXXXXXXX

                Date      Open      High       Low     Close    Volume
      1   2024-11-15  598.9000  607.1500  586.2400  593.5400  10345127
      2   2024-11-08  518.0000  598.6050  510.5100  589.2600  19200378
      3   2024-11-01  515.4000  530.7850  514.5301  519.3500   8098061
      4   2024-10-25  528.0000  529.8500  510.7400  512.6000   8294593
      5   2024-10-18  517.6500  540.5100  515.5100  528.5000  13252133
      6   2024-10-11  494.6300  517.9700  490.7900  516.3000   7884739
      7   2024-10-04  494.8900  496.5150  484.2000  495.1600   7589303
      8   2024-09-27  498.2100  505.4000  490.3950  498.5100   6551043
      9   2024-09-20  481.4300  506.4100  479.4900  498.4300  12158126
      10  2024-09-13  485.5300  494.2800  457.4800  478.9900  11000555
      11  2024-09-06  507.4700  509.7000  477.0600  479.6100   7684736
      12  2024-08-30  512.3400  513.5000  498.4700  510.2500   7035638
      13  2024-08-23  505.0000  512.4400  494.0325  509.4200   6763634
      14  2024-08-16  490.2600  506.9100  483.7500  504.2600   7948548
      15  2024-08-09  447.5900  492.0000  437.3700  490.2600  12533544
      16  2024-08-02  500.4200  517.2600  467.2100  470.6400  17023147
      17  2024-07-26  484.5600  504.0000  482.3808  499.0300   9151221
      18  2024-07-19  480.2500  509.4750  476.3070  484.9300  16811873
      19  2024-07-12  467.6500  483.4400  461.5800  479.8800  13665824
      20  2024-07-05  454.5100  469.8899  454.0100  464.7500   5930205
      ...
      43  2024-01-26  386.3600  392.7300  376.7500  377.7900  14102520
      44  2024-01-19  378.3600  385.0100  372.0700  382.2000  10716399
      45  2024-01-12  385.9700  389.0800  374.6800  377.7500   9592481
      46  2024-01-05  383.0000  389.4700  376.7633  386.4400   9043981
  

2.3) Quantitative Models Module

• Purpose: Develop and integrate advanced financial models.
• Key Capabilities:
  • Implement probabilistic models (e.g., Markov Models, Kalman Filters).
  • Incorporate machine learning techniques (e.g., Neural Networks, Random Forests).
  • Custom model creation for specific quantitative needs.

2.4) Strategies Module

• Purpose: Design and simulate various trading strategies.
• Key Capabilities:
  • Base Strategy Class that provides the basic structure for any kind of trading strategy to be included.
  • Rule-based strategies (e.g., moving averages, mean-reversion) _(TBI)
  • Advanced algorithmic strategies (e.g., pairs trading, arbitrage) (TBI)
  • Custom strategies with user-defined parameters (TBI)

2.5) Backtesting Module

• Purpose: Evaluate trading strategies using historical data.

• Key Capabilities:

  • Event-driven backtesting framework for realistic simulations.
  • Backtesting Engine that performs all main backtesting operations. The engine is an abstraction built on top of other existing backtesting libraries such as Backtesting.py, Backtrader...)
  • Backtesting analysis module that provides:
    • Metrics for performance analysis (e.g., Sharpe Ratio, Maximum Drawdown).
    • Visual analysis charts
  • Optimization module that enables iteration and refinement of strategies.
  • Support for multi-asset and multi-timeframe backtesting.

  Quick Example:

  Below we use test the implementation of a sample crossing moving average strategy and its backtesting, extracted from backtesting.py:

    import backtesting
    import qrde_sat
  
    from qrde_sat.qrde4_strategies import Strategy
    from qrde_sat.qrde5_backtesting import Backtest
    from backtesting.lib import crossover
    from backtesting.test import SMA, GOOG
  
  
    class SmaCross(Strategy):
        n1 = 10
        n2 = 20
  
        def init(self):
            close = self.data.Close
            self.sma1 = self.I(SMA, close, self.n1)
            self.sma2 = self.I(SMA, close, self.n2)
  
        def next(self):
            if crossover(self.sma1, self.sma2):
                self.buy()
            elif crossover(self.sma2, self.sma1):
                self.sell()
  
  
    bt = Backtest(GOOG, SmaCross,
                cash=10000, commission=.002,
                exclusive_orders=True)
  
    output = bt.run()
    bt.plot()

  Output:

      Start                     2004-08-19 00:00:00
      End                       2013-03-01 00:00:00
      Duration                   3116 days 00:00:00
      Exposure Time [%]                       94.27
      Equity Final [$]                     68935.12
      Equity Peak [$]                      68991.22
      Return [%]                             589.35
      Buy & Hold Return [%]                  703.46
      Return (Ann.) [%]                       25.42
      Volatility (Ann.) [%]                   38.43
      Sharpe Ratio                             0.66
      Sortino Ratio                            1.30
      Calmar Ratio                             0.77
      Max. Drawdown [%]                      -33.08
      Avg. Drawdown [%]                       -5.58
      Max. Drawdown Duration      688 days 00:00:00
      Avg. Drawdown Duration       41 days 00:00:00
      # Trades                                   93
      Win Rate [%]                            53.76
      Best Trade [%]                          57.12
      Worst Trade [%]                        -16.63
      Avg. Trade [%]                           1.96
      Max. Trade Duration         121 days 00:00:00
      Avg. Trade Duration          32 days 00:00:00
      Profit Factor                            2.13
      Expectancy [%]                           6.91
      SQN                                      1.78
      _strategy              SmaCross(n1=10, n2=20)
  

2.6) Deployment Module

• Purpose: Deploy strategies to real-world brokers for execution.
• Key Capabilities:
  • Integration with broker APIs (e.g., Interactive Brokers, Alpaca, Robinhood) (TBI)
  • Live trading execution and monitoring (TBI)
  • Modular architecture for easy broker API extensions (TBI)

3) Future Works

Some of the improvements that may be added in future versions of QRDE-SAT include the following:

• Develop detailed documentation for models and API usage under docs folder.
• Finish implementation of all models and functionalities whose status appears as "(TBD)" (i.e. to be implemented) in the summaries described above.
• Include additional quantitative models and use them to develop new personal trading strategies.
• Develop a robust deployment module to trade researched and backtested strategies on live markets.
• Implement tests for all library functionalities under the folder tests.

4) Source Libraries

• Backtesting.py
• Backtrader
• Bt
• finmarketpy
• Zipline
• PyAlgoTrade
• FastQuant