Stock, Currency and Commodity Analysis for Volatility Investing in Python

Here is a small program written in Python (Anaconda newest version) to analyse stocks etc.. It's great to get feeling for the drift and volatility of a specific asset.

# -*- coding: utf-8 -*-
"""
Spyder Editor

"""
PR = 20
START='2005-02-01'
END='2017-03-27'
SYMBOL="dez.de"
SYMBOL2="eur=x"
from pandas_datareader.data import get_data_yahoo
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from numpy import convolve
def movingaverage (values, window):
    weights = np.repeat(1.0, window)/window
    sma = np.convolve(values, weights, 'valid')
    return sma
def EOM(df, n):
    EoM = (df['High'].diff(1) + df['Low'].diff(1)) * (df['High'] - df['Low']) / (2 * df['Volume'])
    Eom_ma = pd.Series(pd.rolling_mean(EoM, n), name = 'EoM')
    df = df.join(Eom_ma)
    return df
def ACCDIST(df, n):
    ad = (2 * df['Close'] - df['High'] - df['Low']) / (df['High'] - df['Low']) * df['Volume']
    M = ad.diff(n - 1)
    N = ad.shift(n - 1)
    ROC = M / N
    AD = pd.Series(ROC, name = 'Acc/Dist_ROC')
    df = df.join(AD)
    return df
def ROC(df, n):
    M = df['Close'].diff(n - 1)
    N = df['Close'].shift(n - 1)
    ROC = pd.Series(M / N*100, name = 'ROC')
    df = df.join(ROC)
    return df
def STDDEV(df, n):
    df = df.join(pd.Series(pd.rolling_std(df['ROC'], n)*15.87450786638754, name = 'STD'))
    return df

datahighlowclosevolume = get_data_yahoo(SYMBOL, start = START, end = END)[['Close','Volume','High','Low']]
"""datahighlowclosevolume2 = get_data_yahoo(SYMBOL2, start = START, end = END)[['Close','Volume','High','Low']]
datahighlowclosevolume['Close']=datahighlowclosevolume['Close']/datahighlowclosevolume2['Close']"""
sma = movingaverage(np.asarray(datahighlowclosevolume['Close']), 20)
beginningzeros=np.repeat(0, 20-1)
fullsma = np.concatenate([beginningzeros,sma])
acc = ACCDIST((datahighlowclosevolume), PR)
datahighlowclosevolume['sma']=fullsma
roc=ROC((datahighlowclosevolume), PR)
rocdaily=ROC((datahighlowclosevolume), 2)
std=STDDEV((rocdaily), PR)
plt.figure(1,figsize=(16, 24))
plt.subplot(211)
plt.plot(datahighlowclosevolume['Close']);
plt.plot(datahighlowclosevolume['sma']);
plt.ylim(datahighlowclosevolume['Close'].min()*0.98,datahighlowclosevolume['Close'].max()*1.02)

plt.figure(2,figsize=(16, 14))
plt.subplot(211)
y_pos = np.arange(len(datahighlowclosevolume['Volume']))
plt.bar(y_pos, datahighlowclosevolume['Volume'], align='center', alpha=0.5)
plt.ylim(acc['Volume'].quantile(.05),acc['Volume'].quantile(.95))    
'''plt.plot(datahighlowclosevolume['Volume'])'''
plt.subplot(212)      
plt.plot(acc['Acc/Dist_ROC'])
plt.ylim(acc['Acc/Dist_ROC'].quantile(.05),acc['Acc/Dist_ROC'].quantile(.95))

plt.figure(3,figsize=(16, 14))
plt.subplot(211)
plt.plot(roc['ROC'])
plt.ylim(roc['ROC'].quantile(.05),roc['ROC'].quantile(.95))
plt.axhline(roc['ROC'].mean())
rocquantp=list(filter(lambda x: x < roc['ROC'].quantile(.05), roc['ROC']))
rocquantn=list(filter(lambda x: x > roc['ROC'].quantile(.95), roc['ROC']))
plt.subplot(212)
plt.plot(std['STD'])
plt.axhline(std['STD'].quantile(.25))
plt.axhline(std['STD'].quantile(.50))
plt.axhline(std['STD'].quantile(.75))
plt.ylim(std['STD'].quantile(.05),std['STD'].quantile(.95))



To run this program you have to install pandas datareader as well. Change the dates and the period (PR) to change the timeframe for all windows. SYMBOL is the symbol yo use. Use yahoo symbols. You can use the program with two symbols as well. This could be beneficial for currency pairs or pairs trading in general.