# In[45]:
plt.plot(tradesCount)
# In[46]:
plt.plot(dtes2Label(dtesUsed), np.cumsum(pnl1), 'k')
plt.plot(dtes2Label(dtesUsed), np.cumsum(pnl2), 'b')
plt.plot(dtes2Label(dtesUsed), np.cumsum(pnl3), 'r')
plt.plot(dtes2Label(dtesUsed), np.cumsum(pnl4), 'c')
plt.plot(dtes2Label(dtesUsed), np.cumsum(pnl5), 'm')
plt.legend([
'第一天入场到第一天收盘', '第一天收盘到第二天开盘', '第二天开盘到第二天收盘', '第二天收盘到第三天开盘', '第三天开盘到第三天收盘'
])
plt.grid()
# In[47]:
rschLib.drawPNL(dtesUsed,
pnl3 + pnl4 + pnl5,
dtes,
strategy_name,
toDatabase='yes')
# In[48]:
rschLib.saveOffStart(strategy_name, off_start)
# In[ ]:
#draw pnl and tag pnl
importlib.reload(rschLib)
[dtesByTrade,
pnlByTrade] = rschLib.getPnlWithCache(dtes,
tkrs,
name,
tradesUsed,
inTime,
otTime,
dayOff,
timeAsFloat,
toDatabase='yes',
strategy_name=strategy_name)
[dtesPnlAggr, pnlAggr,
numTrades] = rschLib.aggregatePnlAndDtes(dtesByTrade, pnlByTrade)
rschLib.drawPNL(dtesPnlAggr, pnlAggr, dtes, strategy_name, toDatabase='yes')
# In[41]:
#importlib.reload(rschLib)
#%load_ext line_profiler
#%lprun -f rschLib.getPnl rschLib.getPnl(dtes,tkrs, name, tradesUsed, inTime, otTime, dayOff, timeAsFloat)
# In[43]:
#draw pnl and tag pnl
tnames, tagNamesEn, t2 = rschLib.getTagNames()
importlib.reload(rschLib)
for i in range(len(tnames)):
tagName = tnames[i]
[dtesWithTag, pnlWithTag,
idxTradable,
dtes,
tkrs,
name,
tradesUsed,
inTime,
otTime,
dayOff,
timeAsFloat,
toDatabase='yes',
strategy_name=strategy_name)
[dtesPnlAggr, pnlAggr,
numTrades] = rschLib.aggregatePnlAndDtes(dtesByTrade, pnlByTrade)
rschLib.drawPNL(dtesPnlAggr,
pnlAggr,
dtes,
strategy_name,
showFigure='yes',
toDatabase='yes')
for i in range(len(tnames)):
tagName = tnames[i]
[dtesWithTag, pnlWithTag,
n] = rschLib.aggregatePnlAndDtes(dtesByTrade[idxOverLapTagList[i]],
pnlByTrade[idxOverLapTagList[i]])
rschLib.drawPNL(dtesWithTag,
pnlWithTag,
dtes,
strategy_name,
showFigure='yes',
toDatabase='yes',
dateStart=dtesPnlAggr[0],
pnlType=tagName)
def analyzeStrategy(strategy_name, offStart, dtes, name, tkrs):
timeAsFloat, timeLabels, maxM, dayOff, dayTimeAsFloat = rschLib.getTimeLabels(
maxD)
R = open_mtx[:, 1:] / close_mtx[:, :-1] - 1 #使用收盘到开盘的回报率来修正分红和拆股
R = np.hstack((np.zeros((R.shape[0], 1)), R))
tradesUsed, r_withnan = rschLib.getTradesFast(strategy_name, name, tkrs,
dtes, maxD, dayTimeAsFloat,
R)
# get trade samples by good/bad trades
tradeArea = [inTime, otTime]
idxTradable = np.isfinite(r_withnan[:, tradeArea[0]])
r = r_withnan.copy()
r[np.isfinite(r) == False] = 0
result = rschLib.getTradeAnalysisSampleGroups(r, idxTradable, tradeArea)
# draw price change
rschLib.drawPriceChange(r[idxTradable, :],
strategy_name,
timeLabels=timeLabels,
tp=tradeArea)
rschLib.drawPriceChange(result['rGood10'],
strategy_name,
timeLabels=timeLabels,
title='盈利前10%交易',
tp=tradeArea)
#rschLib.drawPriceChange(result['rGood20'], strategy_name, timeLabels=timeLabels, title='盈利前20%交易', tp=tradeArea)
rschLib.drawPriceChange(result['rGood30'],
strategy_name,
timeLabels=timeLabels,
title='盈利前30%交易',
tp=tradeArea)
rschLib.drawPriceChange(result['rBad10'],
strategy_name,
timeLabels=timeLabels,
title='亏损前10%交易',
tp=tradeArea)
#rschLib.drawPriceChange(result['rBad20'], strategy_name, timeLabels=timeLabels, title='亏损前20%交易', tp=tradeArea)
rschLib.drawPriceChange(result['rBad30'],
strategy_name,
timeLabels=timeLabels,
title='亏损前30%交易',
tp=tradeArea)
# analyze tags
#rschLib.analyzeTradeTags(tradesUsed, result['rGood10'], result['idxGood10'], '盈利前10%交易',strategy_name, dtes, tkrs, offStart)
#rschLib.analyzeTradeTags(tradesUsed, result['rGood20'], result['idxGood20'], '盈利前20%交易',strategy_name, dtes, tkrs, offStart)
#rschLib.analyzeTradeTags(tradesUsed, result['rGood30'], result['idxGood30'], '盈利前30%交易',strategy_name, dtes, tkrs, offStart)
#rschLib.analyzeTradeTags(tradesUsed, result['rBad10'], result['idxBad10'], '亏损前10%交易',strategy_name, dtes, tkrs, offStart)
#rschLib.analyzeTradeTags(tradesUsed, result['rBad20'], result['idxBad20'], '亏损前20%交易',strategy_name, dtes, tkrs, offStart)
#rschLib.analyzeTradeTags(tradesUsed, result['rBad30'], result['idxBad30'], '亏损前30%交易',strategy_name, dtes, tkrs, offStart)
#get tag names
tnames, tagNamesEn, t2 = rschLib.getTagNames()
idxOverLapTagList = rschLib.analyzeTradeTags(tradesUsed, r,
list(range(len(tradesUsed))),
'所有交易', strategy_name, dtes,
tkrs, offStart,
"d:\\pklWeeklyUpdate\\")
#draw pnl and tag pnl
importlib.reload(rschLib)
[dtesByTrade, pnlByTrade] = rschLib.getPnlFast(r,
dtes,
tkrs,
name,
tradesUsed,
inTime,
otTime,
dayOff,
timeAsFloat,
toDatabase='yes',
strategy_name=strategy_name)
[dtesPnlAggr, pnlAggr,
numTrades] = rschLib.aggregatePnlAndDtes(dtesByTrade, pnlByTrade)
rschLib.drawPNL(dtesPnlAggr,
pnlAggr,
dtes,
strategy_name,
showFigure='no',
toDatabase='yes')
for i in range(len(tnames)):
tagName = tnames[i]
[dtesWithTag, pnlWithTag,
n] = rschLib.aggregatePnlAndDtes(dtesByTrade[idxOverLapTagList[i]],
pnlByTrade[idxOverLapTagList[i]])
rschLib.drawPNL(dtesWithTag,
pnlWithTag,
dtes,
strategy_name,
showFigure='no',
toDatabase='yes',
dateStart=dtesPnlAggr[0],
pnlType=tagName)
rschLib.drawPNL(dtesWithTag,
pnlWithTag,
dtes,
strategy_name + '+' + tagNamesEn[i],
showFigure='no',
toDatabase='yes',
dateStart=dtesPnlAggr[0],
pnlType='pnl')
#analysis of number of trades vs performance
importlib.reload(rschLib)
rschLib.pnlVsNumtrades(pnlAggr, numTrades, strategy_name, toDatabase='yes')
rschLib.saveOffStart(strategy_name, offStart)
rschLib.drawPriceChange(rBad2, strategy_name, timeLabels=timeLabels, title='亏损前20%交易', tp=tradeArea) rschLib.drawPriceChange(rBad3, strategy_name, timeLabels=timeLabels, title='亏损前30%交易', tp=tradeArea) # In[83]: importlib.reload(rschLib) [dtesPnl,pnl, numTrades]=rschLib.getPnl(dtes,tkrs, name, trades, inTime, otTime, dayOff, timeAsFloat, toDatabase='yes') # In[81]: importlib.reload(rschLib) rschLib.drawPNL(dtesPnl, pnl, dtes, strategy_name, toDatabase='yes') # In[57]: importlib.reload(rschLib) rschLib.pnlVsNumtrades(pnl, numTrades) # In[58]: # %load_ext line_profiler # #%lprun -f getPnl getPnl()
def analyzeStrategy(strategy_name, offStart, dtes, name, tkrs):
timeAsFloat, timeLabels, maxM, dayOff = rschLib.getTimeLabels(maxD)
trades, tradesUsed, Po, r = rschLib.getTradesWithPklCache(
strategy_name, name, tkrs, dtes, maxD, maxM)
# get trade samples by good/bad trades
tradeArea = [inTime, otTime]
result = rschLib.getTradeAnalysisSampleGroups(r, tradeArea)
# draw price change
rschLib.drawPriceChange(r,
strategy_name,
timeLabels=timeLabels,
tp=tradeArea)
rschLib.drawPriceChange(result['rGood10'],
strategy_name,
timeLabels=timeLabels,
title='盈利前10%交易',
tp=tradeArea)
rschLib.drawPriceChange(result['rGood20'],
strategy_name,
timeLabels=timeLabels,
title='盈利前20%交易',
tp=tradeArea)
rschLib.drawPriceChange(result['rGood30'],
strategy_name,
timeLabels=timeLabels,
title='盈利前30%交易',
tp=tradeArea)
rschLib.drawPriceChange(result['rBad10'],
strategy_name,
timeLabels=timeLabels,
title='亏损前10%交易',
tp=tradeArea)
rschLib.drawPriceChange(result['rBad20'],
strategy_name,
timeLabels=timeLabels,
title='亏损前20%交易',
tp=tradeArea)
rschLib.drawPriceChange(result['rBad30'],
strategy_name,
timeLabels=timeLabels,
title='亏损前30%交易',
tp=tradeArea)
# analyze tags
#rschLib.analyzeTradeTags(tradesUsed, result['rGood10'], result['idxGood10'], '盈利前10%交易',strategy_name, dtes, tkrs, offStart)
#rschLib.analyzeTradeTags(tradesUsed, result['rGood20'], result['idxGood20'], '盈利前20%交易',strategy_name, dtes, tkrs, offStart)
#rschLib.analyzeTradeTags(tradesUsed, result['rGood30'], result['idxGood30'], '盈利前30%交易',strategy_name, dtes, tkrs, offStart)
#rschLib.analyzeTradeTags(tradesUsed, result['rBad10'], result['idxBad10'], '亏损前10%交易',strategy_name, dtes, tkrs, offStart)
#rschLib.analyzeTradeTags(tradesUsed, result['rBad20'], result['idxBad20'], '亏损前20%交易',strategy_name, dtes, tkrs, offStart)
#rschLib.analyzeTradeTags(tradesUsed, result['rBad30'], result['idxBad30'], '亏损前30%交易',strategy_name, dtes, tkrs, offStart)
#get tag names
tnames, tagNamesEn, t2 = rschLib.getTagNames()
idxOverLapTagList = rschLib.analyzeTradeTags(tradesUsed, r,
list(range(len(tradesUsed))),
'所有交易', strategy_name, dtes,
tkrs, offStart)
#draw pnl and tag pnl
importlib.reload(rschLib)
[dtesByTrade, pnlByTrade] = rschLib.getPnl(dtes,
tkrs,
name,
tradesUsed,
inTime,
otTime,
dayOff,
timeAsFloat,
toDatabase='yes',
strategy_name=strategy_name)
[dtesPnlAggr, pnlAggr,
numTrades] = rschLib.aggregatePnlAndDtes(dtesByTrade, pnlByTrade)
rschLib.drawPNL(dtesPnlAggr,
pnlAggr,
dtes,
strategy_name,
toDatabase='yes')
for i in range(len(tnames)):
tagName = tnames[i]
[dtesWithTag, pnlWithTag,
n] = rschLib.aggregatePnlAndDtes(dtesByTrade[idxOverLapTagList[i]],
pnlByTrade[idxOverLapTagList[i]])
rschLib.drawPNL(dtesWithTag,
pnlWithTag,
dtes,
strategy_name,
toDatabase='yes',
dateStart=dtesPnlAggr[0],
pnlType=tagName)
rschLib.drawPNL(dtesWithTag,
pnlWithTag,
dtes,
strategy_name + '+' + tagNamesEn[i],
toDatabase='yes',
dateStart=dtesPnlAggr[0],
pnlType='pnl')
#analysis of number of trades vs performance
importlib.reload(rschLib)
rschLib.pnlVsNumtrades(pnlAggr, numTrades, strategy_name, toDatabase='yes')
rschLib.saveOffStart(strategy_name, offStart)