#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]
# draw pnl and tag pnl
importlib.reload(rschLib)
[dtesByTrade, pnlByTrade] = rschLib.getPnlFast(r,
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,
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)
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)
# analyze tags
rschLib.analyzeTradeTags(tradesUsed, result['rGood10'], result['idxGood10'], '盈利前10%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['rBad30'], result['idxBad30'], '亏损前30%交易',strategy_name, dtes, tkrs, offStart)
rschLib.analyzeTradeTags(tradesUsed, result['rDieting'], result['idxDieting'], '亏损前30%交易',strategy_name, dtes, tkrs, offStart)
# get tag names
tnames, t1,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.getPnlFast(r, 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)
# 求非涨停，以及伴随有tagName标签的交易，绘制价格变化曲线
idxTradableAndHasTag = list(np.intersect1d(np.nonzero(idxTradable)[0], np.array(idxOverLapTagList[i])))
rschLib.drawPriceChange(r[idxTradableAndHasTag,:], strategy_name, timeLabels=timeLabels, title=tagName, tp=tradeArea)
# control group and optimal group
[rawInTime, rawOtTime] = rschLib.getDefaultTradeTime(offStart)
idxTradableRaw = np.isfinite(r_withnan[:, rawInTime])
[dtesByTradeRaw, pnlByTradeRaw] = rschLib.getPnlFast(r, idxTradableRaw, dtes, tkrs, name, tradesUsed, rawInTime, rawOtTime, dayOff, timeAsFloat, toDatabase='yes', strategy_name=strategy_name)
[dtesPnlAggrRaw, pnlAggrRaw, numTradesRaw] = rschLib.aggregatePnlAndDtes(dtesByTradeRaw, pnlByTradeRaw)