# 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[ ]:
def generateTagAnalysis(tagName):
print(tagName)
tname = rschLib.tagMapper(tagName)['file']
strategy_name = tname
startDate = 20180101
back_test_days = 500
with open("d:\\pkl\\" + tname + ".pkl", 'rb+') as f:
tagInfo = pickle.load(f)
tag_mtx = tagInfo['tag_mtx']
off_start = rschLib.tagMapper(tagName)['off_start']
rschLib.saveOffStart(strategy_name, off_start)
tagNotNew = np.zeros(close_mtx.shape)
for i in range(close_mtx.shape[0]):
j = np.nonzero(close_mtx[i,:]>0)[0][0]
v = np.min((close_mtx.shape[1], j+60))
tagNotNew[i, v:]=1
tag_mtx = (tag_mtx==1) & (tagNotNew==1) # 去掉新股票
max_holding_days = 4
daily_stage = []
matrix_types = ['open_mtx', 'close_mtx']
flag = 0
for i in range(max_holding_days*2):
t = (matrix_types[i%2], int(i/2))
if (t==off_start):
flag = 1
if flag==1:
daily_stage.append((matrix_types[i%2], int(i/2)))
matrixName = {
'open_mtx': '开盘价',
'close_mtx': '收盘价'
}
# %% 画出
plt.figure()
plt.rcParams['font.sans-serif'] = [u'SimHei']
plt.rcParams['axes.unicode_minus'] = False
default_dpi = plt.rcParamsDefault['figure.dpi']
plt.rcParams['figure.dpi'] = default_dpi*2
pnlByPeriod = [] # 在特定时间区间的持股标签平均回报率，如标签产生后的当天开盘到当天收盘，
dtesUsedByPeriod = []
tagsUsedByPeriod = []
legends = []
for i in range(len(daily_stage)-1):
priceIn = tags.TagBase.get_zero2pre(daily_stage[i][0])[:, daily_stage[i][1]:]
priceOt = tags.TagBase.get_zero2pre(daily_stage[i+1][0])[:, daily_stage[i+1][1]:]
priceIn = priceIn[:, :priceOt.shape[1]] # priceIn 和 priceOt头对齐， 如果priceOt长度小于priceIn, 则priceIn取部分
pct_change = rschLib.np_fill_zero(priceOt / priceIn - 1) # 百分比变化
dtesUsed = dtes[daily_stage[i][1]:]
dtesUsed = dtesUsed[:priceOt.shape[1]] # 对齐后的日期
tag_mtxUsed = tag_mtx[:, :priceOt.shape[1]] # 对齐标签矩阵
ret = tag_mtxUsed * pct_change # 回报率矩阵
ret_avg = rschLib.np_fill_zero(ret.sum(axis=0)/tag_mtxUsed.sum(axis=0)) # 计算按tag持股平均回报率
pnlByPeriod.append(ret_avg)
dtesUsedByPeriod.append(dtesUsed)
tagsUsedByPeriod.append(tag_mtxUsed)
plt.plot(np.cumsum(ret_avg))
legends.append('第'+str(daily_stage[i][1])+'天的'+matrixName[daily_stage[i][0]])
pnlByPeriod_mtx = np.zeros((len(pnlByPeriod), len(pnlByPeriod[-1])))
plt.title(tagName+'标签持股区间平均回报率')
plt.legend(legends)
plt.grid()
for i in range(len(pnlByPeriod)):
pnlByPeriod_mtx[i,:] = pnlByPeriod[i][:len(pnlByPeriod[-1])]
# %% calculate best investment records, 如果是开盘的，假设开盘可以买入， 如果是收盘的，假设第二天开盘可以买入
if off_start==('open_mtx', 0):
best = 0
else:
best = 1
idxStart = dtesUsedByPeriod[best]>startDate
idxStart2 = dtesUsedByPeriod[best+1]>startDate
dtesUsed = dtesUsedByPeriod[best][idxStart]
tag_mtxUsed = tagsUsedByPeriod[best][:, idxStart]
n1 = np.cumsum(pnlByPeriod[best][idxStart])
n2 = np.cumsum(pnlByPeriod[best+1][idxStart2])
n1[len(n1)-len(n2):] = n1[len(n1)-len(n2):]+n2
pnl = [float(x) for x in n1]
plt.title('pnl')
plt.plot(pnl)
plt.grid()
labels = [int(x) for x in dtesUsed]
db.strategyBackTest.update_one({'strategy_name': strategy_name}, {'$set':{
'strategy_name':strategy_name,
'rawLabels':labels,
'rawPnl':pnl
}},upsert=True)
# %% priceChange
priceChange = np.mean(pnlByPeriod_mtx[:, -back_test_days:], axis=1)
plt.figure()
plt.plot(np.cumsum(priceChange))
# %% 每笔交易上传至数据库
db.strategyBackTest.remove({'strategy_name':strategy_name})
db.strategyBackTestTrades.remove({'strategy_name':strategy_name})
dtesUsed = [int(x) for x in dtesUsed]
for (i, x) in enumerate(dtesUsed):
q = name[tag_mtxUsed[:,i]==1]
tk = tkrs[tag_mtxUsed[:,i]==1]
query = []
for i, y in enumerate(q):
query.append({
'strategy_name':strategy_name,
'name':y,
'ticker':tk[i],
'dateIn':int(x)
})
if len(query)>0:
db.strategyBackTestTrades.insert_many(query)
rschLib.updateStrategyGeneratingStatus(strategy_name, '生成进度:10%。初始化标签。'+str(datetime.datetime.now()),10)
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)
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)