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Name: alan
Type: User
Bio: let interests take the lead
Location: toronto
alan's Projects
black-scholes-calculator-python
An interactive Python tool for calculating European option prices, probabilities, and implied volatility using the Black-Scholes model.
cluster-based-strategy-selection-python
exploring the clustering of pre-designed trading strategies to see if there is any potential for creating uncorrelated investment portfolios.
clustering-portfolio-opt-python
applying k means clustering to a dataset of the returns of a basket of ETF strategies and returning the best strategy from each cluster in attempt to create a diversified portfolio. also used methods like pca to reduce dimensionality and generated features
cointegration-exploration-python
This project explores pairs trading as a market-neutral strategy by leveraging statistical relationships between cointegrated assets to exploit mean-reverting behavior. inspired and adapted from the quant trading room
deseas-autocorr-time-series-sales-modelling-spython
Analyzing and forecasting retail sales using time series modeling techniques such as Fourier series, calendar effects, and lag features
etf-sql-database-python
An efficiently ETF database management system. when run ,recent data for specific tickers are stored in the SQLite file etf_data. This avoids hammering API. Prices are also adjusted using the adjustment ratio to prep data for backtesting
etfanalyzer
Analyze and compare investments in popular ETF invesments. Choose a starting capital, the ETFs, and a specific date range
feature-eng-house-price-python
feature engineering in Python, focusing on data preprocessing, feature creation, and model evaluation for house price prediction
financial-calculator-java
Performs financial calculator interest calculations in an interactive way using Java
financial-calculator-python
Performs financial calculator interest calculations in an interactive way using Python. This is a full program
fractional-differencing-time-series-python
exploring fractional differencing outlined in advances in financial machine learning chapter 5, which in short allows us to achieve stationarity while retaining as much memory as possible in the time series
hybrid-linreg-xgboost-timeseries-forecast-sales-python
This project focuses on forecasting store sales using a hybrid model that combines Linear Regression and XGBoost models. The hybrid approach aims to capture both linear trends and non-linear patterns in the sales data. The project involves data preprocessing, feature engineering, model training, prediction, and evaluation.
linearalgebra-toolkit-matlab
linear algebra functions such as regression, principal component analysis for dimensionality reduction, subspaces, etc to streamline linear algebra
portfolio-toolkit-streamlit-python
making professional portfolio management methods accessible through point and click. methods such as mean var opt and portfolio rebalancing is available. try the app from the link
trend-seas-linreg-time-series-forecast-sp500-python
detrending and deseasonalizing time series data, and using that to create a forecast of the log data
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