移动平均线指标MA的python实现
移动平均线（MA）具有趋势的特性，它比较平稳，不像日K线会起起落落地震荡。越长期的移动平均线，越能表现稳定的特性。不轻易向上向下，必须等股价趋势的真正明朗。移动平均线说到底是一种趋势追踪工具，便于识别趋势已经终结或者反转，新的趋势是否正在形成。计算公式.N日MA=N日收市价的总和/N(即算术平均数)# Moving Averages Code
# Load the necessary packages and modules
import pandas as pd
import pandas.io.data as web
import matplotlib.pyplot as plt
# Simple Moving Average
def SMA(data, ndays):
SMA = pd.Series(pd.rolling_mean(data['Close'], ndays), name = 'SMA')
data = data.join(SMA)
return data
# Exponentially-weighted Moving Average
def EWMA(data, ndays):
EMA = pd.Series(pd.ewma(data['Close'], span = ndays, min_periods = ndays - 1),
name = 'EWMA_' + str(ndays))
data = data.join(EMA)
return data
# Retrieve the Nifty data from Yahoo finance:
data = web.DataReader('^NSEI',data_source='yahoo',start='1/1/2013', end='1/1/2016')
data = pd.DataFrame(data)
close = data['Close']
# Compute the 50-day SMA for NIFTY
n = 50
SMA_NIFTY = SMA(data,n)
SMA_NIFTY = SMA_NIFTY.dropna()
SMA = SMA_NIFTY['SMA']
# Compute the 200-day EWMA for NIFTY
ew = 200
EWMA_NIFTY = EWMA(data,ew)
EWMA_NIFTY = EWMA_NIFTY.dropna()
EWMA = EWMA_NIFTY['EWMA_200']
# Plotting the NIFTY Price Series chart and Moving Averages below
plt.figure(figsize=(9,5))
plt.plot(data['Close'],lw=1, label='NSE Prices')
plt.plot(SMA,'g',lw=1, label='50-day SMA (green)')
plt.plot(EWMA,'r', lw=1, label='200-day EWMA (red)')
plt.legend(loc=2,prop={'size':11})
plt.grid(True)
plt.setp(plt.gca().get_xticklabels(), rotation=30)
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