stock_data = Functions.merge_with_index_data(stock_data, index_data)
stock_data = stock_data[[
'date', 'code', 'open', 'high', 'low', 'close', 'change'
]]
concat = pd.DataFrame()
for i in range(14, 19):
df = TA_strategy.adx(stock_data, i)
df = df[df['date'] >= pd.to_datetime('2005-01-01')] # 采用2005年起的数据
df.reset_index(inplace=True,
drop=True) # 在计算ADX之后，最早的一部分数据没有对应的值，需要重新排index
# 当pdi上穿mdi，买入，信号为1；当pdi下穿mdi，卖空，信号为0
# 计算ADX指标并得到信号和仓位
df = Functions.cross_both(df, 'pdi', 'mdi')
df = equity_cal.position(df)
df = equity_cal.equity_curve_complete(df)
# df = df[['date', 'code', 'open', 'high', 'low', 'close', 'change', 'volume', 'equity']]
df['ADX_' + str(i)] = df['equity']
df.set_index(keys='date', inplace=True)
concat = pd.concat([concat, df['ADX_' + str(i)]], axis=1, join='outer')
stock_data = stock_data[stock_data['date'] >= pd.to_datetime('2005-01-01')]
stock_data['benchmark'] = (stock_data['change'] + 1).cumprod() * 1000000.00
stock_data.set_index(keys='date', inplace=True)
concat = pd.concat([concat, stock_data['benchmark']], axis=1, join='inner')
fig = plt.figure(figsize=(16, 5))
for i in range(14, 19):
plt.plot(concat['ADX_' + str(i)], label='ADX_' + str(i))
'code', 'start', 'param', 'stock_rtn', 'stock_md', 'strategy_rtn',
'strategy_md', 'excessive_rtn'
])
i = 0
for p in range(16, 18, 2):
for q in range(20, 21):
df = TA_strategy.emv(stock_data, n=p, m=q)
df = df[df['date'] >= pd.to_datetime('2005-01-01')] # 采用2005年起的数据
df.reset_index(inplace=True,
drop=True) # 在计算emv之后，最早的一部分数据没有对应的值，需要重新排index
# EMV大于MAEMV时，买入，信号为1；当EMV小于MAEMV时，卖出，信号为 0
# 计算EMV指标并得到信号和仓位
df = Functions.cross_both(df, 'emv', 'maemv')
df = equity_cal.position(df)
df = equity_cal.equity_curve_complete(df)
df = df[[
'date', 'code', 'open', 'high', 'low', 'close', 'change',
'volume', 'equity'
]]
df['capital_rtn'] = df['equity'].pct_change(1)
df.ix[0, 'capital_rtn'] = 0
df['capital'] = (df['capital_rtn'] + 1).cumprod()
df_stock = stock_data.copy()
# 股票的年化收益
rng_stock = pd.period_range(df_stock['date'].iloc[0],
df_stock['date'].iloc[-1],
freq='D')