MachineLearningStocks
vectorbt
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MachineLearningStocks | vectorbt | |
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57 | 5 | |
1,643 | 3,734 | |
- | - | |
0.0 | 6.2 | |
2 months ago | 11 days ago | |
Python | Python | |
MIT License | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
MachineLearningStocks
- Scikit-learn Stock Prediction: using fundamental and pricing data to predict future stock returns. Sklearn's randomforest classifier is trainded and author claimed positive live trading results. Not actively mainained Other Models - star count:1520.0
- Scikit-learn Stock Prediction: using fundamental and pricing data to predict future stock returns. Sklearn's randomforest classifier is trainded and author claimed positive live trading results. Not actively mainained Other Models - star count:1411.0
vectorbt
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Is there any python libraries to backtest buy and sell signals with dates?
For exactly this I use this https://github.com/polakowo/vectorbt it’s really a powerful tool and you can tons of things with it. Recently the developer decided to maintain it but not adding new features, which from now on will be released on the pro version. However, the free version is still very valuable, incredibly fast and suitable for basic to intermediate tasks.
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Why Building a Trading Algorithm is More Than Just the Algorithm - 3 Things
It’s super easy to get up and running with code. With the rise of data science as a field, datasets are far and wide. Accessible from just about any venue. Take a look at Kaggle, QuiverQuant, Yahoo Finance, or even directly from the brokerages and exchanges. Developers can easily download data directly as a .csv or .json and quickly get up and running by utilizing frameworks like backtesting.py or vectorbt. “Great, it seems like I can get up and running and I’ll have an awesome money making trading algorithm in no time”.... unfortunately, wrong. Why is this wrong? Well, simulation is NOT the real world. The real world is not a CSV file—the real world is a stream of events. Cause and effect. The real world works in a fashion where new data comes in, you make a decision, and then you figure it out, not “I have all of this data, let me run this all through time and figure it out”. Indeed, the data sources that you get in real-time are almost completely different from the data sources you use in simulation. Rather than .csv you use WebSockets; rather than QuiverQuant you use APIs; rather than backtesting frameworks you use more robust, event driven packages. Without it, you’re stuck duplicating code, rewriting it into an event-based system, and ultimately using that to go into production, and who knows if your code is going to change along the way.
- Vectorbt – Find your trading edge
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Repost with explanation - OOS Testing cluster
I second the idea of looking through software optimization, but there is no need to jump right to C. I would look at something like vectorbt. You get the speed of C running under the hood while staying in Python for your back testing code
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Looking for active python backtesting framework
However, it's not the fastest framework. If you need speed, and are good with the data science tool chain in python and the concept of flattening loops into vectorized operations, check out vector-bt. I haven't gotten a chance to play with it yet, but I'm definitely going to as soon as I find some spare time. It seems like a great option with a nicely modernized approach.
What are some alternatives?
zvt - modular quant framework.
backtrader - Python Backtesting library for trading strategies
pybroker - Algorithmic Trading in Python with Machine Learning
backtesting.py - :mag_right: :chart_with_upwards_trend: :snake: :moneybag: Backtest trading strategies in Python.
stock-prediction-deep-neural-learning - Predicting stock prices using a TensorFlow LSTM (long short-term memory) neural network for times series forecasting
fast-trade - low code backtesting library utilizing pandas and technical analysis indicators
CV-compare - Use **AI** to Compare the CV to the job description to beat the ATS (Applicant tracking systems) in order to higher your chances to get the job
jesse - An advanced crypto trading bot written in Python
Sklearn-genetic-opt - ML hyperparameters tuning and features selection, using evolutionary algorithms.
zipline - Zipline, a Pythonic Algorithmic Trading Library
ibkr-options-volatility-trading - Volatility trading using Long and Short Straddle options strategies on Interactive Broker using Yahoo Finance and TWS API
OctoBot - Open source crypto trading bot