def ENE(type='old', code='600281'):
dat = pd.read_csv("../../api/stock/csv/%sSH_5min.csv" % code, encoding='gbk')
dat = dat.fillna(method='pad')
dat['Adj Close'] = dat['Close']
dat = dat.rename(columns={'Open': 'open', 'High': 'high', 'Volume': 'volume', 'Close': 'close', 'Low': 'low'})
dat.index = dat['Date Time']
dat.index.name = 'date'
# dat = dat.ix[17000:18000,:]
feed = dataFramefeed.Feed(frequency=bar.Frequency.MINUTE)
feed.addBarsFromDataFrame("orcl", dat)
if type == 'macd':
myStrategy = mdd.ENE_backtest(feed, 'orcl')
elif type == 'old':
myStrategy = mdd.ENE_backtest(feed, 'orcl')
sharpeRatioAnalyzer = sharpe.SharpeRatio()
myStrategy.attachAnalyzer(sharpeRatioAnalyzer)
returnsAnalyzer = returns.Returns()
myStrategy.attachAnalyzer(returnsAnalyzer)
plt = plotter.StrategyPlotter(myStrategy, True, True, True)
plt.getOrCreateSubplot("returns").addDataSeries("Simple returns", returnsAnalyzer.getReturns())
ds = pyalg_utils.dataSet(myStrategy) # 抽取交易数据集语句，若使用系统自带画图功能则不需要该项
myStrategy.run()
plt.plot()
print('最大回撤：\t%f\t 交易笔数：\t%d\t 盈利笔数：\t%d\n' % (ds.getMaxDrawDown(), ds.getCount(), ds.getProfitableCount()))
print('累计收益：\t%f\t 夏普比率：\t%f\t' % (returnsAnalyzer.getCumulativeReturns()[-1], ds.getSharpeRatio()))
def turtle_test(dataString='pyalg'):
"""
:param dataString:
:return:
"""
feed = yahoofeed.Feed()
feed.addBarsFromCSV(
"orcl", "../../api/stock/csv/orcl-2000.csv") #注意查看该csv的格式，Open为大写
myStrategy = pdr.turtle(feed, "orcl", 20, 10)
# Attach a returns analyzers to the trategy.
returnsAnalyzer = returns.Returns()
myStrategy.attachAnalyzer(returnsAnalyzer)
# Attach the plotter to the strategy.
plt = plotter.StrategyPlotter(myStrategy)
# Plot the simple returns on each bar.
plt.getOrCreateSubplot("returns").addDataSeries(
"Simple returns", returnsAnalyzer.getReturns())
if dataString == 'pyalg_util':
ds = pyalg_utils.dataSet(myStrategy) # 抽取交易数据集语句，若使用系统自带画图功能则不需要该项
myStrategy.run()
myStrategy.info("Final portfolio value: $%.2f" % myStrategy.getResult())
if dataString == 'pyalg_util':
rs = ds.getDefault() # 获取默认的交易信息，dic格式
plot(rs["cumulativeReturns"][:, 0],
rs["cumulativeReturns"][:, 1]) # 简单作图示例
plt.plot()
def turtle_test(loadtype='pgs', dataString='pyalg'):
"""
:param dataString:
:return:
"""
# 从postgres 中加载
if loadtype == 'pgs':
from api.stock.histmd import to_postgres_md as tpd
dat = tpd.get_h_data('600848')
else:
from api.stock.histmd.to_mongodb_md import k_data_dao as tmd
w = tmd.KdataDbCache()
dat = w.get_k_data(index=False,start='2015-02-05', end='2015-02-19',code='300426')
feed = dataFramefeed.Feed()
feed.addBarsFromDataFrame("orcl", dat)
myStrategy = pdr.turtle(feed, "orcl", 20, 10)
# Attach a returns analyzers to the strategy.
returnsAnalyzer = returns.Returns()
myStrategy.attachAnalyzer(returnsAnalyzer)
# Attach the plotter to the strategy.
plt = plotter.StrategyPlotter(myStrategy)
# Plot the simple returns on each bar.
plt.getOrCreateSubplot("returns").addDataSeries("Simple returns", returnsAnalyzer.getReturns())
if dataString == 'pyalg_util':
ds = pyalg_utils.dataSet(myStrategy) # 抽取交易数据集语句，若使用系统自带画图功能则不需要该项
myStrategy.run()
myStrategy.info("Final portfolio value: $%.2f" % myStrategy.getResult())
if dataString == 'pyalg_util':
rs = ds.getDefault() # 获取默认的交易信息，dic格式
plot(rs["cumulativeReturns"][:, 0], rs["cumulativeReturns"][:, 1]) # 简单作图示例
plt.plot()
def vwap(plot):
"""
组合管理示例
:param plot:
:return:
"""
instrument = ["lenovo", "mi"]
vwapWindowSize = 5
threshold = 0.01
# Download the bars.
feed = yahoofeed.Feed()
feed.addBarsFromCSV("lenovo", "../../api/stock/csv/600847.csv")
feed.addBarsFromCSV("mi","../../api/stock/csv/F600848.csv")
strat = pdr.VWAPMomentum(feed, instrument, vwapWindowSize, threshold)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
strat.attachAnalyzer(sharpeRatioAnalyzer)
if plot:
plt = plotter.StrategyPlotter(strat, True, True, True)
# plt.getPortfolioSubplot().addDataSeries("vwap", strat.getVWAP()[instrument[-1]])
ds = pyalg_utils.dataSet(strat) # 抽取交易数据集语句，若使用系统自带画图功能则不需要该项
strat.run()
print("Sharpe ratio: %.2f" % sharpeRatioAnalyzer.getSharpeRatio(0.05))
if plot:
plt.plot()
rs = ds.getReturns() # 获取默认的交易信息，dic格式,可忽略
def kdj(param=[14, 3, 0, 3, 0, 70, 30]):
#dat = ts.get_k_data("000001", start='2006-07-01', end='2017-01-05', index=True)
#dat.to_csv("000002.csv",encoding='gbk',index=False)
dat = pd.read_csv("../../api/stock/csv/000007.csv",
encoding='gbk',
parse_dates=[0],
index_col=0)
dat = dat.fillna(method='pad')
dat['Adj Close'] = dat['close']
dat.index.name = 'date'
dat = dat[['open', 'close', 'high', 'low', 'volume',
'Adj Close']] # the index catch high<open in 2010-02-01
def handle_data(x):
if x[2] < x[0]:
x[2] = x[0]
if x[3] > x[0]:
x[3] = x[0]
return x
dat = dat.apply(handle_data, axis=1)
#print dat.head()
feed = dataFramefeed.Feed()
feed.addBarsFromDataFrame("orcl", dat)
myStrategy = kdj_backtest(feed,
'orcl',
param=param,
df=formular.KDJ(
dat, 14, 3,
3)) #set the param of STOCH and crossover
sharpeRatioAnalyzer = sharpe.SharpeRatio()
myStrategy.attachAnalyzer(sharpeRatioAnalyzer)
returnsAnalyzer = returns.Returns()
myStrategy.attachAnalyzer(returnsAnalyzer)
plt = plotter.StrategyPlotter(myStrategy, True, True, True)
plt.getOrCreateSubplot("returns").addDataSeries(
"Simple returns", returnsAnalyzer.getReturns())
ds = pyalg_utils.dataSet(myStrategy) # 抽取交易数据集语句，若使用系统自带画图功能则不需要该项
myStrategy.run()
#plt.plot()
#slowk,slowd = talib.STOCH(dat.high.values,dat.low.values,dat.close.values,14,3,0,3,0)
#print slowk[-1],slowd[-1]
print(u'drawndown：\t%f\t tradingCount：\t%d\t profitCount：\t%d\n' %
(ds.getMaxDrawDown(), ds.getCount(), ds.getProfitableCount()))
print(u'culmutiveReturn：\t%f\t sharpRatio：\t%f\t' %
(returnsAnalyzer.getCumulativeReturns()[-1], ds.getSharpeRatio()))
def turtle_test(loadtype='tushare', dataString='pyalg'):
"""
:param dataString:
:return:
"""
# 从dataFrame中加载，
if loadtype == 'tushare':
import tushare as ts
dat = ts.get_h_data('600848')
dat.index.name = 'date'
else:
dat = pd.read_csv("../../api/stock/csv/600848.csv",
index_col=0,
encoding='gbk')
print(dat.info())
print(dat.head())
feed = dataFramefeed.Feed()
feed.addBarsFromDataFrame("orcl", dat)
myStrategy = pdr.turtle(feed, "orcl", 20, 10)
# Attach a returns analyzers to the strategy.
returnsAnalyzer = returns.Returns()
myStrategy.attachAnalyzer(returnsAnalyzer)
# Attach the plotter to the strategy.
plt = plotter.StrategyPlotter(myStrategy)
# Plot the simple returns on each bar.
plt.getOrCreateSubplot("returns").addDataSeries(
"Simple returns", returnsAnalyzer.getReturns())
if dataString == 'pyalg_util':
ds = pyalg_utils.dataSet(myStrategy) # 抽取交易数据集语句，若使用系统自带画图功能则不需要该项
myStrategy.run()
myStrategy.info("Final portfolio value: $%.2f" % myStrategy.getResult())
if dataString == 'pyalg_util':
rs = ds.getDefault() # 获取默认的交易信息，dic格式
plot(rs["cumulativeReturns"][:, 0],
rs["cumulativeReturns"][:, 1]) # 简单作图示例
plt.plot()
