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Livermore's Market Key Method

Home Page: http://blog.dynofu.me/post/2014/07/26/jesse-livermore-market-method.html

License: MIT License

Python 15.76% Shell 0.29% Jupyter Notebook 83.60% Makefile 0.35%

lmk's Introduction

LMK

LMK means "Livermore Market Key" which is defined in Jesse Livermore's book "HOW TO TRADE IN STOCKS: The Livermore Formula for Combining Time Element and Price". If you happend to have read the book, it might also be interesting to read http://blog.dynofu.me/post/2014/07/26/jesse-livermore-market-method.html

Does it still work?

Get Started with the code

  • Cell 1 - configure matplotlib
%matplotlib inline

import matplotlib
matplotlib.rcParams['figure.figsize'] = (19, 8)
matplotlib.rcParams['font.family'] = 'Hei'
  • Cell 2 - run it...
from lmk.ticker import Ticker

ticker = Ticker("TSLA")
ticker.retrieve_history("2015-06-01", "2016-04-30")
ticker.visualize("V,C,CL,LMK,WM,PV")

and github renders ipynb files, so here is what the above looks like. https://github.com/dyno/LMK/blob/master/lmk.ipynb

File Layout

.
├── README.md
├── book
│   ├── 1938_1940.py
│   └── 1938_1940.txt
├── lmk
│   ├── __init__.py
│   ├── cache.py
│   ├── calculator
│   │   ├── ATRCalculator.py
│   │   ├── EntryPointCalculator.py
│   │   ├── LMKBandCalculator.py
│   │   ├── ODRCalculator.py
│   │   └── PivotCalculator.py
│   ├── datasource
│   │   ├── DataSource.py
│   │   ├── Google.py
│   │   ├── NetEase.py
│   │   └── Yahoo.py
│   ├── market
│   │   ├── China.py
│   │   ├── Market.py
│   │   └── US.py
│   ├── test
│   │   ├── __init__.py
│   │   ├── test_calculator.py
│   │   ├── test_datasource.py
│   │   ├── test_market.py
│   │   └── test_utils.py
│   ├── ticker.py
│   └── utils.py
├── run.md
├── Makefile
└── scripts
    ├── launchd_wrapper.sh
    ├── org.jupyter.server.plist
    └── run_docker.sh

Code Highlight

  • NetEase.py - Get China market data with better quality than Yahoo/Google.
  • PivotCalculator.py - An algorithm to calculate local crest/trough.

TODO List

  • multi-tickers in one graph (unlikely...)

  • zipline ...

  • the cache layer see Market.py/cache.py

lmk's People

Contributors

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lmk's Issues

咨询算法参数

老师您好,我学习您这个算法一年多时间,但是由于能力有限一直不能理解,想咨询一下 需要调节哪些参数来优化,我把atf-factor参数调节到2.5发现是比较好用的,其他的会比较敏感 大概5%波动就进去下降趋势。老师能提供一下优化算法方法吗

咨询算法参数

老师您好,我学习您这个算法一年多时间,但是由于能力有限一直不能理解,想咨询一下 需要调节哪些参数来优化,我把atf-factor参数调节到2.5发现是比较好用的,其他的会比较敏感 大概5%波动就进去下降趋势。老师能提供一下优化算法方法吗 @

咨询算法参数

老师您好,我学习您这个算法一年多时间,但是由于能力有限一直不能理解,想咨询一下 需要调节哪些参数来优化,我把atf-factor参数调节到2.5发现是比较好用的,其他的会比较敏感 大概5%波动就进去下降趋势。老师能提供一下优化算法方法吗

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