machine-learning-for-trading
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machine-learning-for-trading
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Libraries for synthetic data?
Wondering if anyone has a favorite python library for generating synthetic price data? Ideally I'm looking for something that allows me to generate specific conditions like "bearish" "bullish" "choppy" "volatile" and so on, maybe even throw in some spikes to represent FED events, so my algo can experience a larger amount of out-of-dataset conditions. From what I've found so far, it seems like TimeGAN might be a good option.
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As someone who has zero knowledge in coding, and algo trading—but has more than 7 years of experience in manually trading, where should I start?
I would highly recommend the book Machine Learning for Algorithmic Trading: https://github.com/stefan-jansen/machine-learning-for-trading
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Papers for intro to Statistical Arbitrage
You have to be a little careful with ML if you’re putting it to use, a couple books that give some good practices are https://ml4trading.io/ and https://www.wiley.com/en-gb/Advances+in+Financial+Machine+Learning-p-9781119482086 .
DataScienceProjects
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Tracking mentions began in Dec 2020.
What are some alternatives?
SDV - Synthetic data generation for tabular data
Mad-Money-Backtesting - Backtesting recommendations from Mad Money and "The Cramer Effect/Bounce"
zero-to-mastery-ml - All course materials for the Zero to Mastery Machine Learning and Data Science course.
documentation - This repository contains the documentation for the current Quantiacs project. Check it out at: https://quantiacs.com/documentation/en/
cryptocurrency-price-prediction - Cryptocurrency Price Prediction Using LSTM neural network
TTM - # TTM Stock squeeze detection based on bollinger + keltner channels. Detects which stocks are out after bollinger is inside keltner channel.
fraud-detection-handbook - Reproducible Machine Learning for Credit Card Fraud Detection - Practical Handbook
SynthDet - SynthDet - An end-to-end object detection pipeline using synthetic data
ydata-profiling - 1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
gs-quant - Python toolkit for quantitative finance
FinancePy - A Python Finance Library that focuses on the pricing and risk-management of Financial Derivatives, including fixed-income, equity, FX and credit derivatives.
ydata-synthetic - Synthetic data generators for tabular and time-series data