Mapping function to dataframe

import datetime
import pandas as pd
import pandas.io.data
from pandas import Series, DataFrame
import matplotlib.pyplot as plt
import matplotlib as mpl
import matplotlib.ticker as mticker
import matplotlib.dates as mdates
from matplotlib import style
import numpy as np
import time
import math
import zipline as zp
from zipline.finance.slippage import FixedSlippage


style.use('ggplot')

def outlier_fixing(stock_name,ma1=100,ma2=250,ma3=500,ma4=5000):

    df = pd.read_csv('X:/stocks_sentdex_dates_short.csv', index_col='time', parse_dates=True)
    print df.head()

    df = df[df.type == stock_name.lower()]

    std = pd.rolling_std(df['close'], 25, min_periods=1)
    print std

    df['std'] = pd.rolling_std(df['close'], 25, min_periods=1)

    df = df[df['std'] < 17]
    
    MA1 = pd.rolling_mean(df['value'], ma1)
    MA2 = pd.rolling_mean(df['value'], ma2)
    MA3 = pd.rolling_mean(df['value'], ma3)
    MA4 = pd.rolling_mean(df['value'], ma4)
    
    ax1 = plt.subplot(3, 1, 1)
    df['close'].plot(label='Price')
    
    ax2 = plt.subplot(3, 1, 2, sharex = ax1)
    MA1.plot(label=(str(ma1)+'MA'))
    MA2.plot(label=(str(ma2)+'MA'))
    MA3.plot(label=(str(ma3)+'MA'))
    MA4.plot(label=(str(ma4)+'MA'))

    ax3 = plt.subplot(3, 1, 3, sharex = ax1)
    df['std'].plot(label='Deviation')

    plt.legend()
    plt.show()
    
#outlier_fixing('btcusd',ma1=100,ma2=2500,ma3=5000,ma4=50000)

def single_stock(stock_name,ma1=100,ma2=250,ma3=500,ma4=5000):

    df = pd.read_csv('X:/stocks_sentdex_dates_full.csv', index_col='time', parse_dates=True)
    print df.head()

    df = df[df.type == stock_name.lower()]

    MA1 = pd.rolling_mean(df['value'], ma1)
    MA2 = pd.rolling_mean(df['value'], ma2)
    MA3 = pd.rolling_mean(df['value'], ma3)
    MA4 = pd.rolling_mean(df['value'], ma4)
    

    ax1 = plt.subplot(2, 1, 1)
    df['close'].plot(label='Price')
    
    ax2 = plt.subplot(2, 1, 2, sharex = ax1)
    MA1.plot(label=(str(ma1)+'MA'))
    MA2.plot(label=(str(ma2)+'MA'))
    MA3.plot(label=(str(ma3)+'MA'))
    MA4.plot(label=(str(ma4)+'MA'))

    plt.legend()
    plt.show()


# new function

def calc_position(ma1, ma2, ma3, ma4):
    if ma4 > ma1 > ma2 > ma3:
        return 1
    elif ma1 > ma4 > ma2 > ma3:
        return 2
    elif ma1 > ma2 > ma4 > ma3:
        return 3
    elif ma1 > ma2 > ma3 > ma4:
        return 4
    elif ma1 < ma2 < ma3 < ma4:
        return -4
    elif ma1 < ma2 < ma4 < ma3:
        return -3
    elif ma1 < ma4 < ma2 < ma3:
        return - 2
    elif ma4 < ma1 < ma2 < ma3:
        return -1
    else:
        return None


def single_stock_auto_MA(stock_name, div1=275, div2=110, div3=55, div4=5.5):


    df = pd.read_csv('X:/stocks_sentdex_dates_full.csv', index_col='time', parse_dates=True)
    
    df = df[df.type == stock_name.lower()]
    count = df['type'].value_counts()
    count = int(count[stock_name])

    MA1 = pd.rolling_mean(df['value'], (count/div1))
    MA2 = pd.rolling_mean(df['value'], (count/div2))
    MA3 = pd.rolling_mean(df['value'], (count/div3))
    MA4 = pd.rolling_mean(df['value'], (count/div4))

    SP = int(math.ceil(count/div4))

    df['MA1'] = MA1
    df['MA2'] = MA2
    df['MA3'] = MA3
    df['MA4'] = MA4

    df = df[SP:]
    del df['MA100']
    del df['MA250']
    del df['MA500']
    del df['MA5000']

    df['Pos'] = map(calc_position, df['MA1'],df['MA2'],df['MA3'],df['MA4'])

    df['Change'] = df['Pos'].diff()




    #####################
    #####################
    #####################
    #####################

    ax1 = plt.subplot(2, 1, 1)
    df['close'].plot(label='Price')

    ax2 = plt.subplot(2, 1, 2, sharex = ax1)


    df['MA1'].plot(label=(str(count/div1)+'MA'))
    df['MA2'].plot(label=(str(count/div2)+'MA'))
    df['MA3'].plot(label=(str(count/div3)+'MA'))
    df['MA4'].plot(label=(str(int(round((count/div4), 0)))+'MA'))

    plt.legend()
    plt.show()


single_stock_auto_MA('bac')




		

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