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
- http://blessedfool.blogspot.com/2013/05/project-freedom-12-livermore-secret.html
- TODO: backtest with zipline
- 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
.
├── 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
NetEase.py- Get China market data with better quality than Yahoo/Google.PivotCalculator.py- An algorithm to calculate local crest/trough.
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multi-tickers in one graph (unlikely...)
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zipline ...
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the cache layersee Market.py/cache.py