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Simple_Bollinger_Bands_Backtest
These Python code tests a Bollinger bands strategy in the VIX index, indicating Buy & Sell signals,Greeks_Demonstration_Project
In the last month, Caio and I developed a Python script capable of deriving the Black & Scholes equation and finding all Greeks' equations, including Third-degree Greeks. In addition, we developed the graphic representation of these Greeks' behavior to variations in terms of time, implied volatility, dividends, risk-free rate, and stock price. Moreover, in this study, we found new important greeks that weren't cataloged before. As an example, we named one of them as ''Tau'' (It's demonstrated and analyzed at the end of this report)Greeks-Calculator
These code have the objetive to calculate all the greeks in a real option contract ( using the Black&Scholes model), greeks like Delta,Theta,Vega,Gamma,Rho,Episilon,Veta,Vomma,Vanna,Speed,Zomma,Color,Ultima,Dual Delta, Dual GammaPortfolio-Sharpe-Calculator
These Python Code receives stocks and weights of an portfolio as input and returns the Sharpe Index, covariance, variance, portfolio volatility & expected return as outputsCrypto-Gamma-Scalping-
Datathon ProjectReturns_Probability
These Python code uses the T- Student distribution to estimate a return percentage in a stock over n days, if you have these answer you can use Kelly Criterion in many options structures.Portfolio-Optimization
These code have the objective to maximize the Sharpe Index of a portfolio, using the Efficient Markowitz Frontier these code return the perfect weights of each stock in your portfolio trying to improve your expected return and decrease your portfolio volatilityOptions_Payoff
Using Opstrat to visualize Options Strategies PayoffsAggregate-Implied-Volatility
These Pythin Code calculate the Aggregate Implied Volatility of a stock based on "Exploiting Earnings Volatility"- Brian JohnsonsSimple_MACD_Backtest
These Python code uses a simple MACD strategy based on the crossover of the MACD line and the Signal line, the conclusion of the backtest is that the real gain (without the commissions) is worse than a simple Buy&Hold positionWiener_Process_Study_Simulation
This Python code generates a lot of monte carlo simulations using the Wiener process with the SP500 data, the stochastics curves fit very well with the real world dataDiscrete_Delta
This Python code show the difference between an Normal Delta and a modification on that greek propoused by Nassim Taleb in "Dynamic Hedging"Aroon_Oscilator_Backtest
These Python code tests an aroon oscilator strategy in some stocks, one sucessful example was on Brazilian stock Petrobras (PETR4.SA) beating the Buy & Hold StrategyHistorical-Volatility-Calculator
These Python Project have the objective to calculate the Historical Volatility on an asset based on the theory of "Exploiting Earnings Volatilty" - Brian Johnson pg.17 - pg.19SMA_Relation_Backtest
Python code to backtest an strategy using the relation between 2 SMA, great results in some individual stocks, explanation video : https://www.youtube.com/watch?v=-uVkWBRp_FwSimple_SMA_Strategy
These Python code uses the Simple Moving Average of 20 days and buy SP500 when the price crosses above these moving average and sell SP500 when the pricess crosses below the moving average, we can observe a win against a simple Buy & Hold Strategy and these code is already considering comissions ($0.29)Put_Call_Ratio_Backtest
This Python code backtests an strategy based on a put call ratio signal. results: Annual returns = 22.51%, Maximum drawdown = -0,51% and a 80% Hit Ratio. The sharpe index of this strategy beat the SP500 Buy & HoldBenford-Law-In-Collatz-Conjecture
Proving that the first digit of each value in each collatz conjecture chain with 1 billion trys tend to the values in Benford LawAlgorithms
Algorithms portfolioCox_Ross_Rubinstein_Option_Pricing
CRR Binomial tree to american style option pricingMonopoly_Game_Frequencies
This is just a funny project to analyse the percentages of each street stop in a monopoly game simultator, to use that code in real life you can get the yield paid by each street and multiply by the percentage of each street frequency to analyse what are the best streets to buyOption_Pricing_Monte_Carlo
Monte Carlo Variance Reduction for option pricingGarman-Klass-Volatility
Calculation of Realized Volatility by Garman Klass MethodConvergence_BTOPM_BSOPM
Convergence between BTOPM and BSOPMKolmogorov-Smirnov-Test
Test the normality of returns in brazilian stocksHouse_Price_Prediction_Machine_Learning
Using a dataset for Indian House Prices from Kaggle I have developed an Machine Learning Algorithm to predict house pricesStock-Beta
These project have the objective to calculate the beta of an a stock by dividing the Covariance by the market variance, the next idea to upgrade these system consists to calculate the beta by a linear regression modelLove Open Source and this site? Check out how you can help us