Discover guiregueira/Aggregate-Implied-Volatility Open Source project

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These Pythin Code calculate the Aggregate Implied Volatility of a stock based on "Exploiting Earnings Volatility"- Brian Johnsons

Simple_Bollinger_Bands_Backtest

These Python code tests a Bollinger bands strategy in the VIX index, indicating Buy & Sell signals,

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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)

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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 Gamma

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Portfolio-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 outputs

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Crypto-Gamma-Scalping-

Datathon Project

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Returns_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.

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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 volatility

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Options_Payoff

Using Opstrat to visualize Options Strategies Payoffs

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Simple_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 position

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Wiener_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 data

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Discrete_Delta

This Python code show the difference between an Normal Delta and a modification on that greek propoused by Nassim Taleb in "Dynamic Hedging"

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IV_Newton_Raphson

Using the Newton Raphson method this Python code can estimate the implied volatility in a option contract. The necessary inputs are: Spot price,Strike,Time to expiration in years and risk free rate

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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 Strategy

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Historical-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.19

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SMA_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_Fw

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Simple_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 & Hold

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Benford-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 Law

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Algorithms

Algorithms portfolio

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Cox_Ross_Rubinstein_Option_Pricing

CRR Binomial tree to american style option pricing

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Monopoly_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 buy

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Option_Pricing_Monte_Carlo

Monte Carlo Variance Reduction for option pricing

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Garman-Klass-Volatility

Calculation of Realized Volatility by Garman Klass Method

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Convergence_BTOPM_BSOPM

Convergence between BTOPM and BSOPM

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Kolmogorov-Smirnov-Test

Test the normality of returns in brazilian stocks

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House_Price_Prediction_Machine_Learning

Using a dataset for Indian House Prices from Kaggle I have developed an Machine Learning Algorithm to predict house prices

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Stock-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 model

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guiregueira/Aggregate-Implied-Volatility

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