PyCon 2016Beat Stock Index Return: use of Technical

Analysis, Machine Learning, Sentiment and Back-Testing

Talks @parthasen

Referenceshttp://pandas.pydata.org/pandas-docs/stable/io.html

http://gbeced.github.io/pyalgotrade/docs/v0.17/html/tutorial.html#trading

Part A: 8 min

1. Introduction: EMH and frequency of data.

2. Downloading open source data from yahoo.

3. Use of pandas,numpy to read data, analysis and input-output in csv format from hard disk.

4. Technical analysis and plotting data using Matplotlib

Data

Open source data available from Yahoo but daily data

Minute, Second or Tick data available from paid databases.

My Talks will be using daily data loaded from yahoo and minute data from paid databases.

Downloading Yahoo! data

In future pandas releases (0.17+) pandas-datareader will become a dependancy and using pandas.io.data will be equivalent to using pandas_datareader.data.For now, you must replace your imports from pandas.io with pandas_datareader:

$ pip install pandas-datareader

import pandas.io.data as web

from pandas_datareader import data as web

import pandas_datareader as pdr

pdr.get_data_yahoo('AAPL')

https://pandas-datareader.readthedocs.io/en/latest/

Dataset from 2008-01-01 to 2016-07-15

Open High Low Close Volume Adj CloseDate 2016-07-11 855.0 862.00 853.00 860.19 84100 860.192016-07-12 865.0 866.00 860.19 865.32 30100 865.322016-07-13 864.9 867.00 860.15 865.48 112700 865.482016-07-14 870.0 870.51 864.00 869.44 63400 869.442016-07-15 875.0 875.00 867.00 868.04 33000 868.04

Open High Low Close Volume Adj CloseDate 2008-01-01 619.5 622.00 618.0 619.99 2200 608.422008-01-02 622.0 625.00 609.5 622.94 16100 611.312008-01-03 625.0 627.00 616.0 621.31 9700 609.712008-01-04 627.0 637.45 620.1 633.45 7100 621.632008-01-07 625.1 633.50 625.0 633.00 10400 621.18

Daily ploting of NIFTYBEES from 2008

Daily ploting of NIFTYBEES from 2015

Log Return

Cumulative return from 2008

Volatility

Part B: 8 min

1. Downloading tweets and application of tweets for sentiment analysis

2. Application of scikit-learn for machine learning

3. Selection of best technique.

4. Regression analysis for prediction of price.

Data Set for Machine Learning

Open High Low Close Volume Adj Close Open Change \Date 2008-03-19 471 481.0 462.10 463.22 0.541796 454.57 1 2008-03-24 461 472.0 454.95 463.28 2.290142 454.63 -1 2008-03-25 473 493.0 473.00 490.17 1.084630 481.02 1 2008-03-26 492 494.0 486.05 489.15 0.588848 480.02 1 2008-03-27 487 491.7 483.00 488.27 0.641767 479.16 -1 US_Mkt Volatility 3UD momentum RSI 14d \Date 2008-03-19 1 0.236933 0 -31.03 24.415525 489.195714 2008-03-24 1 0.235345 0 -7.67 24.452459 484.429286 2008-03-25 1 0.244263 0 11.59 38.869415 482.062857 2008-03-26 1 0.251964 0 30.53 38.568750 480.787857 2008-03-27 -1 0.259302 0 26.76 38.293538 480.195714 42d Cross BS2 momentum_ RSI_ Change Date 2008-03-19 513.050476 -1 1 -1 -1 NaN 2008-03-24 511.333571 -1 1 -1 -1 -0.004804 2008-03-25 511.204286 -1 0 0 -1 0.020764 2008-03-26 510.429048 -1 0 0 -1 0.003726 2008-03-27 509.925000 -1 0 0 -1 -0.004405

Histogram of Features

Histogram of target

SVM regression

Plotting of features