zipline
quantstats
| zipline | quantstats | |
|---|---|---|
| 14 | 9 | |
| 17,471 | 4,736 | |
| 0.8% | - | |
| 0.0 | 0.0 | |
| 7 months ago | about 2 months ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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zipline
- Ask HN: How to Get into Quantitative Trading?
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Open source backtesting software
https://github.com/quantopian/zipline (event-driven)
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10 FinTech APIs every Indian developer should bookmark
Zipline by Quantopian: An Open-Source tool for algorithmic trading. It is a platform for developing and testing quantitative trading strategies using Python.
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Backtesting Engine Design Primers
For personal use only. I'm currently looking at QuantConnect's LEAN and Quantopian's Zipline (which hasn't seen any updates since 2020, presumably because Quantopian was dissolved).
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[D] Doing my (bachelor) thesis on RL. Which topic do you like best?
(1) I remember there were decent libraries for this setting a while back. Maybe take a look at Quantopian/Zipline.
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Best Backtesting Libraries (Python)
zipline – Zipline is a Pythonic algorithmic trading library. It is an event-driven system that supports both backtesting and live trading.
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How to statistically compare the performance of two strategies?
I found two opensource tools 1. .https://github.com/quantopian/zipline Quantopian 2. https://analyzingalpha.com/backtrader-backtesting-trading-strategies backtrader
- Formula for slippage?
- Online Portfolio Selection - Research paper implementation and backtest
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Best Backtesting software?
Some of the notable libraries in Python are backtesting.py, bt and zipline. Personally I like bt the most, as its tree model makes the most intuitive sense.
quantstats
- Quantstats issue with Google Colab
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Excel (or other) summary stats for algo performance?
IMO can’t do better than QuantStats https://github.com/ranaroussi/quantstats
- QuantStats: Portfolio Analytics for Quants
- Backtesting Results
- Python: which are good modules for strategy evaluation?
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Beyond sample reports
https://github.com/ranaroussi/quantstats Many technical metrics for detailed performance
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successful bots easy
I have a bot that does 100x in 3 years. But it is only 60 % accurate. You don't need high accuracy. In the end what counts is cumulative returns when you include trading costs (even if zero fees you still have slippage). Sharpe, Sortino, daily returns, drawdowns you have to look at all these measures. Use this and it does a pretty in depth report of your trading strategy. https://github.com/ranaroussi/quantstats
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What are the top Python finance libraries?
Quantstats for generating backtest reports: https://github.com/ranaroussi/quantstats
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Important python libraries?
quantstats to analyze the performance of strategies
What are some alternatives?
backtrader - Python Backtesting library for trading strategies
finta - Common financial technical indicators implemented in Pandas.
pyfolio - Portfolio and risk analytics in Python
Portfolio-Report-Generator - A program which allows the user to enter positions and their allocation to get return metrics and data on the underlying positions.
backtrader - Python Backtesting library for trading strategies [Moved to: https://github.com/mementum/backtrader]
fast-trade - low code backtesting library utilizing pandas and technical analysis indicators
PyThalesians - Python library for backtesting trading strategies & analyzing financial markets (formerly pythalesians)
qtpylib - QTPyLib, Pythonic Algorithmic Trading
vectorbt - Find your trading edge, using the fastest engine for backtesting, algorithmic trading, and research.
Empyrial - AI and data-driven quantitative portfolio management library for portfolio risk and performance analysis 投资组合管理
qlib - Qlib is an AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms. including supervised learning, market dynamics modeling, and RL.
alphalens - Performance analysis of predictive (alpha) stock factors