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Multi-Factor
Multi-Factor
 
 
Random_Forests_Project
Random_Forests_Project
 
 
Sentiment Research
Sentiment Research
 
 
52 Week Break Out Trendfollowing Notebook for Public Use.ipynb
52 Week Break Out Trendfollowing Notebook for Public Use.ipynb
 
 
Backtest a Simple Bollinger Band Strategy and creating adjusted Open High Low.py
Backtest a Simple Bollinger Band Strategy and creating adjusted Open High Low.py
 
 
Bitwise analysis and portfolios.R
Bitwise analysis and portfolios.R
 
 
Buy on 52 week highs, sell on lows, optimize, test significance.py
Buy on 52 week highs, sell on lows, optimize, test significance.py
 
 
Buy on the Dips.py
Buy on the Dips.py
 
 
Creating Monte Carlo Distribution on stock price to backtest validity of strategy.py
Creating Monte Carlo Distribution on stock price to backtest validity of strategy.py
 
 
FF_daily.CSV
FF_daily.CSV
 
 
Fund Attribution via Factors-Public.ipynb
Fund Attribution via Factors-Public.ipynb
 
 
HedgeEye four quadrant inflationa and gdp test (public).R
HedgeEye four quadrant inflationa and gdp test (public).R
 
 
Historical Vol Target Strategy with For Loops SMA vs. EMA (public).py
Historical Vol Target Strategy with For Loops SMA vs. EMA (public).py
 
 
Kernal Density Estimators and Fat Tail Distribution Estimation.R
Kernal Density Estimators and Fat Tail Distribution Estimation.R
 
 
Measuring Factor Attribution for a Portfolio (Public).py
Measuring Factor Attribution for a Portfolio (Public).py
 
 
Monte Carlo Simulations to test of significance of 200 SMA strategy.py
Monte Carlo Simulations to test of significance of 200 SMA strategy.py
 
 
Optimize Rolling Equal Risk Contribution Port Optim with FF Sectors.py
Optimize Rolling Equal Risk Contribution Port Optim with FF Sectors.py
 
 
Performance_Analysis.py
Performance_Analysis.py
 
 
QI Strategy 1 - 3 SMAs Trend Backtest.py
QI Strategy 1 - 3 SMAs Trend Backtest.py
 
 
QI Strategy 2 - buy if down several days and sell backtest.py
QI Strategy 2 - buy if down several days and sell backtest.py
 
 
QI Strategy 3 - RSI overbought over sold with Stop Loss and Take Profit.py
QI Strategy 3 - RSI overbought over sold with Stop Loss and Take Profit.py
 
 
QI Strategy 4 - Turtles BreakOut Strategy using Object Oriented Programming (OOP).py
QI Strategy 4 - Turtles BreakOut Strategy using Object Oriented Programming (OOP).py
 
 
QI Strategy 5 - Buy on 20 day high, sell on 20 day low, optimize backtest.py
QI Strategy 5 - Buy on 20 day high, sell on 20 day low, optimize backtest.py
 
 
README.md
README.md
 
 
Replicating Meb Faber Momentum and Trend Strategy (Public).py
Replicating Meb Faber Momentum and Trend Strategy (Public).py
 
 
Seeing if the Fed Balance Sheet Change Affects Stock Prices.py
Seeing if the Fed Balance Sheet Change Affects Stock Prices.py
 
 
Simulated Historical Portfolio Performance with a Specified Sharpe Ratios for Public Use.py
Simulated Historical Portfolio Performance with a Specified Sharpe Ratios for Public Use.py
 
 
Testing Hyperbolic Trend Extension.py
Testing Hyperbolic Trend Extension.py
 
 
Testing if main technical Indicators work (public).R
Testing if main technical Indicators work (public).R
 
 
Testing to see if historical change in earnings affects future stock prices (Public).R
Testing to see if historical change in earnings affects future stock prices (Public).R
 
 
Trendfollowing Strategy with parameter optimization and heatmap using seaborn.py
Trendfollowing Strategy with parameter optimization and heatmap using seaborn.py
 
 
Trendfollowing strategy optimizing sharpe ratio (Public).py
Trendfollowing strategy optimizing sharpe ratio (Public).py
 
 

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Public_Research_and_Backtests

Research and Backtests I have been working on for public viewing. Some in Python and R and some of it is purely technical analysis while other is fundamentally driven.

There are three comprehensive projects, namely, Random Forests and Value Investing, Ensemble ML models for Short Term Trading, and Multi-Factor Equity investing.

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Research and Backtests I have been working on...enjoy

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  • Python 59.8%
  • Jupyter Notebook 33.6%
  • R 6.6%