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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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