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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
pd.options.mode.chained_assignment = None
Exponential Moving Average: Is a type of infinite impulse response filter that applies weighting factors which decrease exponentially. The weighting for each older datum decreases exponentially, never reaching zero.
MACD: The Moving Average Convergence/Divergence oscillator (MACD) is one of the simplest and most effective momentum indicators available. The MACD turns two trend-following indicators, moving averages, into a momentum oscillator by subtracting the longer moving average from the shorter moving average.
Stochastics oscillator: The Stochastic Oscillator is a momentum indicator that shows the location of the close relative to the high-low range over a set number of periods.
Average True Range: Is an indicator to measure the volalitility (NOT price direction). The largest of:
Calculation:
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def MACD(df,period1,period2,periodSignal):
EMA1 = pd.DataFrame.ewm(df,span=period1).mean()
EMA2 = pd.DataFrame.ewm(df,span=period2).mean()
MACD = EMA1-EMA2
Signal = pd.DataFrame.ewm(MACD,periodSignal).mean()
Histogram = MACD-Signal
return Histogram
def stochastics_oscillator(df,period):
l, h = pd.DataFrame.rolling(df, period).min(), pd.DataFrame.rolling(df, period).max()
k = 100 * (df - l) / (h - l)
return k
def ATR(df,period):
'''
Method A: Current High less the current Low
'''
df['H-L'] = abs(df['High']-df['Low'])
df['H-PC'] = abs(df['High']-df['Price'].shift(1))
df['L-PC'] = abs(df['Low']-df['Price'].shift(1))
TR = df[['H-L','H-PC','L-PC']].max(axis=1)
return TR.to_frame()
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df = pd.read_csv('BTCUSD.csv',usecols=[1,2,3,4])
df = df.iloc[::-1]
df["Price"] = (df["Price"].str.split()).apply(lambda x: float(x[0].replace(',', '')))
df["Open"] = (df["Open"].str.split()).apply(lambda x: float(x[0].replace(',', '')))
df["High"] = (df["High"].str.split()).apply(lambda x: float(x[0].replace(',', '')))
df["Low"] = (df["Low"].str.split()).apply(lambda x: float(x[0].replace(',', '')))
dfPrices = pd.read_csv('BTCUSD.csv',usecols=[1])
dfPrices = dfPrices.iloc[::-1]
dfPrices["Price"] = (dfPrices["Price"].str.split()).apply(lambda x: float(x[0].replace(',', '')))
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dfPrices.head(2)
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price = dfPrices.iloc[len(dfPrices.index)-60:len(dfPrices.index)].as_matrix().ravel()
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prices = dfPrices.iloc[len(dfPrices.index)-60:len(dfPrices.index)].as_matrix().ravel()
plt.figure(figsize=(25,7))
plt.plot(prices,label='Test',color='black')
plt.title('Price')
plt.legend(loc='upper left')
plt.show()
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macd = MACD(dfPrices.iloc[len(dfPrices.index)-60:len(dfPrices.index)],12,26,9)
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plt.figure(figsize=(25,7))
plt.plot(macd,label='macd',color='red')
plt.title('MACD')
plt.legend(loc='upper left')
plt.show()
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stochastics = stochastics_oscillator(dfPrices.iloc[len(dfPrices.index)-60:len(dfPrices.index)],14)
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plt.figure(figsize=(14,7))
#First 100 points because it's too dense
plt.plot(stochastics[0:100],label='Stochastics Oscillator',color='blue')
plt.title('Stochastics Oscillator')
plt.legend(loc='upper left')
plt.show()
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atr = ATR(df.iloc[len(df.index)-60:len(df.index)],14)
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plt.figure(figsize=(21,7))
#First 100 points because it's too dense
plt.plot(atr[0:100],label='ATR',color='green')
plt.title('Average True Range')
plt.legend(loc='upper left')
plt.show()
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dfPriceShift = dfPrices.shift(-1)
dfPriceShift.rename(columns={'Price':'PriceTarget'}, inplace=True)
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dfPriceShift.head(2)
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macd = MACD(dfPrices,12,26,9)
macd.rename(columns={'Price':'MACD'}, inplace=True)
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stochastics = stochastics_oscillator(dfPrices,14)
stochastics.rename(columns={'Price':'Stochastics'}, inplace=True)
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atr = ATR(df,14)
atr.rename(columns={0:'ATR'}, inplace=True)
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final_data = pd.concat([dfPrices,dfPriceShift,macd,stochastics,atr], axis=1)
# Delete the entries with missing values (where the stochastics couldn't be computed yet) because have a lot of datapoints ;)
final_data = final_data.dropna()
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final_data.info()
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final_data
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final_data.to_csv('BTCUSD_TechnicalIndicators.csv',index=False)