def main(code, start_date, end_date):
data = getData(code, start_date, end_date)
if data is None: return
feed = dataFramefeed.Feed()
feed.addBarsFromDataFrame(code, data)
# Set Strategy
brk = genBroker(feed)
bands = [-0.40, -0.30, -0.20, -0.10, 0.10, 0.20, 0.30, 0.40]
weights = [1.00, 0.90, 0.70, 0.60, 0.50, 0.35, 0.25, 0.05, 0.00]
#base_price = np.mean(data.close) * 0.95
base_day = 10
myStrategy = GridSearchStrategy(feed, brk, code, base_day, weights, bands)
# Attach a returns analyzers to the strategy
returnsAnalyzer = returns.Returns()
myStrategy.attachAnalyzer(returnsAnalyzer)
# Attach a sharpe ratio analyzers to the strategy
sharpeRatioAnalyzer = sharpe.SharpeRatio()
myStrategy.attachAnalyzer(sharpeRatioAnalyzer)
# Attach the plotter to the strategy
plt = plotter.StrategyPlotter(myStrategy, True, True, True)
plt.getOrCreateSubplot("returns").addDataSeries(
"Simple returns", returnsAnalyzer.getReturns())
# Run Strategy
myStrategy.run()
myStrategy.info("Final portfolio value: $%.2f" % myStrategy.getResult())
plt.plot()
def main(mode = ct.PAPER_TRADING, start_date = '20170815', end_date = '20171113'):
if mode == ct.PAPER_TRADING:
cash = 100000
beta = 9.49
mean = -0.282
std = 34.73
feed = dataFramefeed.Feed()
instruments = ['300296', '300613']
fpath = '/Users/hellobiek/Documents/workspace/python/quant/smart_deal_tool/configure/tushare.json'
ts_client = get_tushare_client(fpath)
for code in instruments:
df = ts.pro_bar(pro_api = ts_client, ts_code = add_suffix(code), adj = 'qfq', start_date = start_date, end_date = end_date)
df = df.rename(columns = {"ts_code": "code", "trade_date": "date", "vol": "volume", "pct_change": "pchange"})
df['date'] = df.date.apply(lambda x: time.strftime('%Y-%m-%d', time.strptime(x, "%Y%m%d")))
df = df.set_index("date")
feed.addBarsFromDataFrame(code, df)
# broker setting
# broker commission类设置
broker_commission = broker.backtesting.TradePercentage(0.002)
# fill strategy设置
fill_stra = broker.fillstrategy.DefaultStrategy(volumeLimit = 1.0)
sli_stra = broker.slippage.NoSlippage()
fill_stra.setSlippageModel(sli_stra)
# 完善broker类
brk = broker.backtesting.Broker(cash, feed, broker_commission)
brk.setFillStrategy(fill_stra)
pStrategy = PairTradingStrategy(feed, instruments, brk, beta, mean, std, cash)
returnsAnalyzer = sreturn.Returns()
pStrategy.attachAnalyzer(returnsAnalyzer)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
pStrategy.attachAnalyzer(sharpeRatioAnalyzer)
drawDownAnalyzer = drawdown.DrawDown()
pStrategy.attachAnalyzer(drawDownAnalyzer)
tradesAnalyzer = trades.Trades()
pStrategy.attachAnalyzer(tradesAnalyzer)
plt = plotter.StrategyPlotter(pStrategy)
# Plot the simple returns on each bar.
plt.getOrCreateSubplot("returns").addDataSeries("Simple returns", returnsAnalyzer.getReturns())
pStrategy.run()
plt.plot()
print("Final portfolio value: $%.2f" % pStrategy.getResult())
print("Cumulative returns: %.2f %%" % (returnsAnalyzer.getCumulativeReturns()[-1] * 100))
print("Sharpe ratio: %.2f" % (sharpeRatioAnalyzer.getSharpeRatio(0.05)))
print("Max. drawdown: %.2f %%" % (drawDownAnalyzer.getMaxDrawDown() * 100))
print("Longest drawdown duration: %s" % (drawDownAnalyzer.getLongestDrawDownDuration()))
def grid_search(code, start_date, end_date):
data = get_data(code, start_date, end_date)
if data is None: return
feed = dataFramefeed.Feed()
feed.addBarsFromDataFrame(code, data)
brk = gen_broker(feed)
local.run(KDJStrategy,
feed,
parameters_generator(code, brk, data),
workerCount=2)
def get_feed(all_df, codes, start_date, end_date, peried, duaration):
feed = dataFramefeed.Feed()
for code in codes:
data = all_df.loc[all_df.code == code]
data = data.sort_values(by=['date'], ascending=True)
data = data.reset_index(drop=True)
data = data.set_index('date')
if is_df_has_unexpected_data(data): return None, None
data.index = pd.to_datetime(data.index)
data = data.dropna(how='any')
data = atr(data, ndays=duaration)
feed.addBarsFromDataFrame(code, data)
return feed
def get_feed(all_df, code, start_date, end_date, peried):
feed = dataFramefeed.Feed()
#cindex_obj = CIndex(code, dbinfo = ct.OUT_DB_INFO, redis_host = '127.0.0.1')
#cstock_obj = CStock(code, dbinfo = ct.OUT_DB_INFO, redis_host = '127.0.0.1')
#data = cstock_obj.get_k_data_in_range(start_date, end_date)
#data = data.set_index('date')
data = all_df.loc[all_df.code == code]
data = data.sort_values(by=['date'], ascending=True)
data = data.reset_index(drop=True)
data = data.set_index('date')
if is_df_has_unexpected_data(data): return None, None
data.index = pd.to_datetime(data.index)
data = data.dropna(how='any')
data = atr(data)
feed.addBarsFromDataFrame(code, data)
return feed
def plate_momentum(mode=ct.PAPER_TRADING,
start_date='2018-03-01',
end_date='2018-10-28'):
if mode == ct.PAPER_TRADING:
threshold = 100000
feed = dataFramefeed.Feed()
instruments = choose_plate()
if len(instruments): return 0
for code in instruments:
data = CStock(code,
should_create_influxdb=False,
should_create_mysqldb=False).get_k_data_in_range(
start_date, end_date)
data = data.set_index('date')
feed.addBarsFromDataFrame(code, data)
# broker setting
# broker commission类设置
broker_commission = broker.backtesting.TradePercentage(0.002)
# fill strategy设置
fill_stra = broker.fillstrategy.DefaultStrategy(volumeLimit=1.0)
sli_stra = broker.slippage.NoSlippage()
fill_stra.setSlippageModel(sli_stra)
# 完善broker类
brk = broker.backtesting.Broker(threshold * len(instruments), feed,
broker_commission)
brk.setFillStrategy(fill_stra)
myStrategy = PlateMomentumStrategy(feed, instruments, brk, threshold)
# 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())
myStrategy.run()
myStrategy.info("Final portfolio value: $%.2f" % myStrategy.getResult())
plt.plot()
def main(code, start_date, end_date):
data = get_data(code, start_date, end_date)
if data is None: return
feed = dataFramefeed.Feed()
feed.addBarsFromDataFrame(code, data)
# Set Strategy
brk = gen_broker(feed)
myStrategy = KDJStrategy(feed, brk, data, code, 45, 100, -0.05, 0.1, 5, 5,
1.5, 1.2)
# Attach a returns analyzers to the strategy
returnsAnalyzer = returns.Returns()
myStrategy.attachAnalyzer(returnsAnalyzer)
# Attach a sharpe ratio analyzers to the strategy
sharpeRatioAnalyzer = sharpe.SharpeRatio()
myStrategy.attachAnalyzer(sharpeRatioAnalyzer)
# Attach the plotter to the strategy
plt = plotter.StrategyPlotter(myStrategy, True, True, True)
plt.getOrCreateSubplot("returns").addDataSeries(
"Simple returns", returnsAnalyzer.getReturns())
# Run Strategy
myStrategy.run()
myStrategy.info("Final portfolio value: $%.2f" % myStrategy.getResult())
plt.plot()