ta
TimeSynth
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| ta | TimeSynth | |
|---|---|---|
| 10 | 1 | |
| 4,028 | 326 | |
| - | 1.8% | |
| 6.9 | 0.0 | |
| 29 days ago | 6 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | MIT License |
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.
ta
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Basic trading patterns, we should all know 😊
If you know python, you’ll be able to see for yourself. Bukosabino took the time to make dozens of indicators easy to calculate from historical data. Try and find any predictive power from any of these on any remotely liquid ticker
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Technical Momentum
Use the technical analysis python library to compute the technical indicators for that SPX data
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Using Bid/Ask data for technical analysis
The documentation can be found here: https://technical-analysis-library-in-python.readthedocs.io/en/latest/
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Binance technical indicators from kline data
I also use python binance but with bubosabino‘s ta library: https://github.com/bukosabino/ta
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Programmer here - Is there a way to code if price of any pair is touching the 50ma on any timeframe?
There's a Python Technical Analysis module located here: https://github.com/bukosabino/ta
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How do I make a bot know if criteria for a trade is met?
If you are using python, check out https://github.com/bukosabino/ta
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Python Module for technical indicators
I'd recommend you to use TA. I used it to generate lany indicators in the past months. No issues to mention, and it's documentation is rich and kept updated. Repo: https://github.com/bukosabino/ta
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An awesome list about crypto trading bots : find open source crypto trading bots, technical analysis and market data libraries, data providers, APIs, ...
I use https://technical-analysis-library-in-python.readthedocs.io/en/latest/ for my proprietary bot.
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How simple/complex are your successful strategies?
Python with a library that gives you tons of indicators to test (personally, I use this one: https://github.com/bukosabino/ta). Pandas is used but just to set the data, nothing fancy. I don't use Jupyter, I go with the simple IDLE that comes with Python installation but this point doesn't really matter, just go with the most comfortable for you. In the end, this is what I get after a backtest: https://i.ibb.co/pjbZvhv/backtest-sample.png
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Is there a tutorial on how to use the TA-Lib library?
I'm using this one - https://github.com/bukosabino/ta
TimeSynth
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What is the best way to generate synthetic OHLC data?
I have the same question so I cant give a direct answer. However, I've been thinking of using SDV and TimeSynth python packages to produce synthetic data for backtesting.
What are some alternatives?
pandas-ta - Technical Analysis Indicators - Pandas TA is an easy to use Python 3 Pandas Extension with 150+ Indicators
tsfresh - Automatic extraction of relevant features from time series:
ta-lib-python - Python wrapper for TA-Lib (http://ta-lib.org/).
SDV - Synthetic data generation for tabular data
python-binance - Binance Exchange API python implementation for automated trading
tempo - API for manipulating time series on top of Apache Spark: lagged time values, rolling statistics (mean, avg, sum, count, etc), AS OF joins, downsampling, and interpolation
borb-google-colab-examples - This repository contains some examples of using borb in google colab. These examples enable you to try out the features of borb without installing it on your system. They also ensure the system requirements and imports are all taken care of.
stingray - Anything can happen in the next half hour (including spectral timing made easy)!
ydata-quality - Data Quality assessment with one line of code
pycaret - An open-source, low-code machine learning library in Python
documentation - This repository contains the documentation for the current Quantiacs project. Check it out at: https://quantiacs.com/documentation/en/
tsai - Time series Timeseries Deep Learning Machine Learning Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai