聲明：本文策略源碼均來(lái)自掘金量化示例策略庫(kù)，僅供參考瞬痘！
一、股票策略
# coding=utf-8
from__future__importprint_function, absolute_import, unicode_literals
importnumpyasnp
fromgm.apiimport*
frompandasimportDataFrame
'''
本策略每隔1個(gè)月定時(shí)觸發(fā),根據(jù)Fama-French三因子模型對(duì)每只股票進(jìn)行回歸板熊，得到其alpha值框全。
假設(shè)Fama-French三因子模型可以完全解釋市場(chǎng)，則alpha為負(fù)表明市場(chǎng)低估該股干签，因此應(yīng)該買(mǎi)入津辩。
策略思路：
計(jì)算市場(chǎng)收益率、個(gè)股的賬面市值比和市值,并對(duì)后兩個(gè)進(jìn)行了分類(lèi),
根據(jù)分類(lèi)得到的組合分別計(jì)算其市值加權(quán)收益率、SMB和HML.
對(duì)各個(gè)股票進(jìn)行回歸(假設(shè)無(wú)風(fēng)險(xiǎn)收益率等于0)得到alpha值.
選取alpha值小于0并為最小的10只股票進(jìn)入標(biāo)的池
平掉不在標(biāo)的池的股票并等權(quán)買(mǎi)入在標(biāo)的池的股票
回測(cè)數(shù)據(jù):SHSE.000300的成份股
回測(cè)時(shí)間:2017-07-01 08:00:00到2017-10-01 16:00:00
'''
definit(context):
# 每月第一個(gè)交易日的09:40 定時(shí)執(zhí)行algo任務(wù)
schedule(schedule_func=algo, date_rule='1m', time_rule='09:40:00')
print(order_target_percent(symbol='SHSE.600000', percent=0.5, order_type=OrderType_Market,
? ? ? ? ? ? ? ? ? ? ? ? position_side=PositionSide_Long))
# 數(shù)據(jù)滑窗
context.date =20
# 設(shè)置開(kāi)倉(cāng)的最大資金量
context.ratio =0.8
# 賬面市值比的大/中/小分類(lèi)
context.BM_BIG =3.0
context.BM_MID =2.0
context.BM_SMA =1.0
# 市值大/小分類(lèi)
context.MV_BIG =2.0
context.MV_SMA =1.0
# 計(jì)算市值加權(quán)的收益率,MV為市值的分類(lèi),BM為賬目市值比的分類(lèi)
defmarket_value_weighted(stocks, MV, BM):
? ? select = stocks[(stocks.NEGOTIABLEMV == MV) & (stocks.BM == BM)]
market_value = select['mv'].values
? ? mv_total = np.sum(market_value)
mv_weighted = [mv / mv_totalformvinmarket_value]
stock_return = select['return'].values
# 返回市值加權(quán)的收益率的和
? ? return_total = []
foriinrange(len(mv_weighted)):
? ? ? ? return_total.append(mv_weighted[i] * stock_return[i])
? ? return_total = np.sum(return_total)
returnreturn_total
defalgo(context):
# 獲取上一個(gè)交易日的日期
last_day = get_previous_trading_date(exchange='SHSE', date=context.now)
# 獲取滬深300成份股
context.stock300 = get_history_constituents(index='SHSE.000300', start_date=last_day,
end_date=last_day)[0]['constituents'].keys()
# 獲取當(dāng)天有交易的股票
? ? not_suspended = get_history_instruments(symbols=context.stock300, start_date=last_day, end_date=last_day)
not_suspended = [item['symbol']foriteminnot_suspendedifnotitem['is_suspended']]
fin = get_fundamentals(table='tq_sk_finindic', symbols=not_suspended, start_date=last_day, end_date=last_day,
fields='PB,NEGOTIABLEMV', df=True)
# 計(jì)算賬面市值比,為P/B的倒數(shù)
fin['PB'] = (fin['PB'] **-1)
# 計(jì)算市值的50%的分位點(diǎn),用于后面的分類(lèi)
size_gate = fin['NEGOTIABLEMV'].quantile(0.50)
# 計(jì)算賬面市值比的30%和70%分位點(diǎn),用于后面的分類(lèi)
bm_gate = [fin['PB'].quantile(0.30), fin['PB'].quantile(0.70)]
? ? fin.index = fin.symbol
? ? x_return = []
# 對(duì)未停牌的股票進(jìn)行處理
forsymbolinnot_suspended:
# 計(jì)算收益率
close = history_n(symbol=symbol, frequency='1d', count=context.date +1, end_time=last_day, fields='close',
skip_suspended=True, fill_missing='Last', adjust=ADJUST_PREV, df=True)['close'].values
stock_return = close[-1] / close[0] -1
pb = fin['PB'][symbol]
market_value = fin['NEGOTIABLEMV'][symbol]
# 獲取[股票代碼. 股票收益率, 賬面市值比的分類(lèi), 市值的分類(lèi), 流通市值]
ifpb < bm_gate[0]:
ifmarket_value < size_gate:
? ? ? ? ? ? ? ? label = [symbol, stock_return, context.BM_SMA, context.MV_SMA, market_value]
else:
? ? ? ? ? ? ? ? label = [symbol, stock_return, context.BM_SMA, context.MV_BIG, market_value]
elifpb < bm_gate[1]:
ifmarket_value < size_gate:
? ? ? ? ? ? ? ? label = [symbol, stock_return, context.BM_MID, context.MV_SMA, market_value]
else:
? ? ? ? ? ? ? ? label = [symbol, stock_return, context.BM_MID, context.MV_BIG, market_value]
elifmarket_value < size_gate:
? ? ? ? ? ? label = [symbol, stock_return, context.BM_BIG, context.MV_SMA, market_value]
else:
? ? ? ? ? ? label = [symbol, stock_return, context.BM_BIG, context.MV_BIG, market_value]
iflen(x_return) ==0:
? ? ? ? ? ? x_return = label
else:
? ? ? ? ? ? x_return = np.vstack([x_return, label])
stocks = DataFrame(data=x_return, columns=['symbol','return','BM','NEGOTIABLEMV','mv'])
? ? stocks.index = stocks.symbol
columns = ['return','BM','NEGOTIABLEMV','mv']
forcolumnincolumns:
? ? ? ? stocks[column] = stocks[column].astype(np.float64)
# 計(jì)算SMB.HML和市場(chǎng)收益率
# 獲取小市值組合的市值加權(quán)組合收益率
? ? smb_s = (market_value_weighted(stocks, context.MV_SMA, context.BM_SMA) +
? ? ? ? ? ? market_value_weighted(stocks, context.MV_SMA, context.BM_MID) +
market_value_weighted(stocks, context.MV_SMA, context.BM_BIG)) /3
# 獲取大市值組合的市值加權(quán)組合收益率
? ? smb_b = (market_value_weighted(stocks, context.MV_BIG, context.BM_SMA) +
? ? ? ? ? ? market_value_weighted(stocks, context.MV_BIG, context.BM_MID) +
market_value_weighted(stocks, context.MV_BIG, context.BM_BIG)) /3
? ? smb = smb_s - smb_b
# 獲取大賬面市值比組合的市值加權(quán)組合收益率
hml_b = (market_value_weighted(stocks, context.MV_SMA,3) +
market_value_weighted(stocks, context.MV_BIG, context.BM_BIG)) /2
# 獲取小賬面市值比組合的市值加權(quán)組合收益率
? ? hml_s = (market_value_weighted(stocks, context.MV_SMA, context.BM_SMA) +
market_value_weighted(stocks, context.MV_BIG, context.BM_SMA)) /2
? ? hml = hml_b - hml_s
close = history_n(symbol='SHSE.000300', frequency='1d', count=context.date +1,
end_time=last_day, fields='close', skip_suspended=True,
fill_missing='Last', adjust=ADJUST_PREV, df=True)['close'].values
market_return = close[-1] / close[0] -1
? ? coff_pool = []
# 對(duì)每只股票進(jìn)行回歸獲取其alpha值
forstockinstocks.index:
x_value = np.array([[market_return], [smb], [hml], [1.0]])
y_value = np.array([stocks['return'][stock]])
# OLS估計(jì)系數(shù)
coff = np.linalg.lstsq(x_value.T, y_value)[0][3]
? ? ? ? coff_pool.append(coff)
# 獲取alpha最小并且小于0的10只的股票進(jìn)行操作(若少于10只則全部買(mǎi)入)
stocks['alpha'] = coff_pool
stocks = stocks[stocks.alpha <0].sort_values(by='alpha').head(10)
? ? symbols_pool = stocks.index.tolist()
? ? positions = context.account().positions()
# 平不在標(biāo)的池的股票
forpositioninpositions:
symbol = position['symbol']
ifsymbolnotinsymbols_pool:
order_target_percent(symbol=symbol, percent=0, order_type=OrderType_Market,
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? position_side=PositionSide_Long)
print('市價(jià)單平不在標(biāo)的池的', symbol)
# 獲取股票的權(quán)重
? ? percent = context.ratio / len(symbols_pool)
# 買(mǎi)在標(biāo)的池中的股票
forsymbolinsymbols_pool:
? ? ? ? order_target_percent(symbol=symbol, percent=percent, order_type=OrderType_Market,
? ? ? ? ? ? ? ? ? ? ? ? ? ? position_side=PositionSide_Long)
print(symbol,'以市價(jià)單調(diào)多倉(cāng)到倉(cāng)位', percent)
if__name__ =='__main__':
'''
? ? strategy_id策略ID,由系統(tǒng)生成
? ? filename文件名,請(qǐng)與本文件名保持一致
? ? mode實(shí)時(shí)模式:MODE_LIVE回測(cè)模式:MODE_BACKTEST
? ? token綁定計(jì)算機(jī)的ID,可在系統(tǒng)設(shè)置-密鑰管理中生成
? ? backtest_start_time回測(cè)開(kāi)始時(shí)間
? ? backtest_end_time回測(cè)結(jié)束時(shí)間
? ? backtest_adjust股票復(fù)權(quán)方式不復(fù)權(quán):ADJUST_NONE前復(fù)權(quán):ADJUST_PREV后復(fù)權(quán):ADJUST_POST
? ? backtest_initial_cash回測(cè)初始資金
? ? backtest_commission_ratio回測(cè)傭金比例
? ? backtest_slippage_ratio回測(cè)滑點(diǎn)比例
? ? '''
run(strategy_id='strategy_id',
filename='main.py',
? ? ? ? mode=MODE_BACKTEST,
token='token_id',
backtest_start_time='2017-07-01 08:00:00',
backtest_end_time='2017-10-01 16:00:00',
? ? ? ? backtest_adjust=ADJUST_PREV,
backtest_initial_cash=10000000,
backtest_commission_ratio=0.0001,
backtest_slippage_ratio=0.0001)
# coding=utf-8
from__future__importprint_function, absolute_import, unicode_literals
try:
importtalib
except:
print('請(qǐng)安裝TA-Lib庫(kù)')
fromgm.apiimport*
'''
本策略首先買(mǎi)入SHSE.600000股票10000股
隨后根據(jù)60s的數(shù)據(jù)來(lái)計(jì)算MACD(12,26,9)線(xiàn),并在MACD>0的時(shí)候買(mǎi)入100股,MACD<0的時(shí)候賣(mài)出100股
但每日操作的股票數(shù)不超過(guò)原有倉(cāng)位,并于收盤(pán)前把倉(cāng)位調(diào)整至開(kāi)盤(pán)前的倉(cāng)位
回測(cè)數(shù)據(jù)為:SHSE.600000的60s數(shù)據(jù)
回測(cè)時(shí)間為:2017-09-01 08:00:00到2017-10-01 16:00:00
'''
definit(context):
# 設(shè)置標(biāo)的股票
context.symbol ='SHSE.600000'
# 用于判定第一個(gè)倉(cāng)位是否成功開(kāi)倉(cāng)
context.first =0
# 訂閱浦發(fā)銀行, bar頻率為1min
subscribe(symbols=context.symbol, frequency='60s', count=35)
# 日內(nèi)回轉(zhuǎn)每次交易100股
context.trade_n =100
# 獲取昨今天的時(shí)間
context.day = [0,0]
# 用于判斷是否觸發(fā)了回轉(zhuǎn)邏輯的計(jì)時(shí)
context.ending =0
defon_bar(context, bars):
bar = bars[0]
ifcontext.first ==0:
# 最開(kāi)始配置倉(cāng)位
# 需要保持的總倉(cāng)位
context.total =10000
# 購(gòu)買(mǎi)10000股浦發(fā)銀行股票
? ? ? ? order_volume(symbol=context.symbol, volume=context.total, side=PositionSide_Long,
? ? ? ? ? ? ? ? ? ? order_type=OrderType_Market, position_effect=PositionEffect_Open)
print(context.symbol,'以市價(jià)單開(kāi)多倉(cāng)10000股')
context.first =1.
day = bar.bob.strftime('%Y-%m-%d')
context.day[-1] = day[-2:]
# 每天的倉(cāng)位操作
context.turnaround = [0,0]
return
# 更新最新的日期
day = bar.bob.strftime('%Y-%m-%d %H:%M:%S')
context.day[0] = bar.bob.day
# 若為新的一天,獲取可用于回轉(zhuǎn)的昨倉(cāng)
ifcontext.day[0] != context.day[-1]:
context.ending =0
context.turnaround = [0,0]
ifcontext.ending ==1:
return
# 若有可用的昨倉(cāng)則操作
ifcontext.total >=0:
# 獲取時(shí)間序列數(shù)據(jù)
symbol = bar['symbol']
recent_data = context.data(symbol=symbol, frequency='60s', count=35, fields='close')
# 計(jì)算MACD線(xiàn)
macd = talib.MACD(recent_data['close'].values)[0][-1]
# 根據(jù)MACD>0則開(kāi)倉(cāng),小于0則平倉(cāng)
ifmacd >0:
# 多空單向操作都不能超過(guò)昨倉(cāng)位,否則最后無(wú)法調(diào)回原倉(cāng)位
ifcontext.turnaround[0] + context.trade_n < context.total:
# 計(jì)算累計(jì)倉(cāng)位
context.turnaround[0] += context.trade_n
? ? ? ? ? ? ? ? order_volume(symbol=context.symbol, volume=context.trade_n, side=PositionSide_Long,
? ? ? ? ? ? ? ? ? ? ? ? ? ? order_type=OrderType_Market, position_effect=PositionEffect_Open)
print(symbol,'市價(jià)單開(kāi)多倉(cāng)', context.trade_n,'股')
elifmacd <0:
ifcontext.turnaround[1] + context.trade_n < context.total:
context.turnaround[1] += context.trade_n
? ? ? ? ? ? ? ? order_volume(symbol=context.symbol, volume=context.trade_n, side=PositionSide_Short,
? ? ? ? ? ? ? ? ? ? ? ? ? ? order_type=OrderType_Market, position_effect=PositionEffect_Close)
print(symbol,'市價(jià)單平多倉(cāng)', context.trade_n,'股')
# 臨近收盤(pán)時(shí)若倉(cāng)位數(shù)不等于昨倉(cāng)則回轉(zhuǎn)所有倉(cāng)位
ifday[11:16] =='14:55'orday[11:16] =='14:57':
? ? ? ? ? ? position = context.account().position(symbol=context.symbol, side=PositionSide_Long)
ifposition['volume'] != context.total:
? ? ? ? ? ? ? ? order_target_volume(symbol=context.symbol, volume=context.total, order_type=OrderType_Market,
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? position_side=PositionSide_Long)
print('市價(jià)單回轉(zhuǎn)倉(cāng)位操作...')
context.ending =1
# 更新過(guò)去的日期數(shù)據(jù)
context.day[-1] = context.day[0]
if__name__ =='__main__':
'''
? ? strategy_id策略ID,由系統(tǒng)生成
? ? filename文件名,請(qǐng)與本文件名保持一致
? ? mode實(shí)時(shí)模式:MODE_LIVE回測(cè)模式:MODE_BACKTEST
? ? token綁定計(jì)算機(jī)的ID,可在系統(tǒng)設(shè)置-密鑰管理中生成
? ? backtest_start_time回測(cè)開(kāi)始時(shí)間
? ? backtest_end_time回測(cè)結(jié)束時(shí)間
? ? backtest_adjust股票復(fù)權(quán)方式不復(fù)權(quán):ADJUST_NONE前復(fù)權(quán):ADJUST_PREV后復(fù)權(quán):ADJUST_POST
? ? backtest_initial_cash回測(cè)初始資金
? ? backtest_commission_ratio回測(cè)傭金比例
? ? backtest_slippage_ratio回測(cè)滑點(diǎn)比例
? ? '''
run(strategy_id='strategy_id',
filename='main.py',
? ? ? ? mode=MODE_BACKTEST,
token='token_id',
backtest_start_time='2017-09-01 08:00:00',
backtest_end_time='2017-10-01 16:00:00',
? ? ? ? backtest_adjust=ADJUST_PREV,
backtest_initial_cash=2000000,
backtest_commission_ratio=0.0001,
backtest_slippage_ratio=0.0001)
# coding=utf-8
from__future__importprint_function, absolute_import, unicode_literals
importnumpyasnp
fromgm.apiimport*
frompandasimportDataFrame
'''
本策略以0.8為初始權(quán)重跟蹤指數(shù)標(biāo)的滬深300中權(quán)重大于0.35%的成份股.
個(gè)股所占的百分比為(0.8*成份股權(quán)重)*100%.然后根據(jù)個(gè)股是否:
1.連續(xù)上漲5天 2.連續(xù)下跌5天
來(lái)判定個(gè)股是否為強(qiáng)勢(shì)股/弱勢(shì)股,并對(duì)其把權(quán)重由0.8調(diào)至1.0或0.6
回測(cè)時(shí)間為:2017-07-01 08:50:00到2017-10-01 17:00:00
'''
definit(context):
# 資產(chǎn)配置的初始權(quán)重,配比為0.6-0.8-1.0
context.ratio =0.8
# 獲取滬深300當(dāng)時(shí)的成份股和相關(guān)數(shù)據(jù)
stock300 = get_history_constituents(index='SHSE.000300', start_date='2017-06-30', end_date='2017-06-30')[0][
'constituents']
? ? stock300_symbol = []
? ? stock300_weight = []
forkeyinstock300:
# 保留權(quán)重大于0.35%的成份股
if(stock300[key] /100) >0.0035:
? ? ? ? ? ? stock300_symbol.append(key)
stock300_weight.append(stock300[key] /100)
context.stock300 = DataFrame([stock300_weight], columns=stock300_symbol, index=['weight']).T
print('選擇的成分股權(quán)重總和為: ', np.sum(stock300_weight))
subscribe(symbols=stock300_symbol, frequency='1d', count=5, wait_group=True)
defon_bar(context, bars):
# 若沒(méi)有倉(cāng)位則按照初始權(quán)重開(kāi)倉(cāng)
forbarinbars:
symbol = bar['symbol']
? ? ? ? position = context.account().position(symbol=symbol, side=PositionSide_Long)
ifnotposition:
buy_percent = context.stock300['weight'][symbol] * context.ratio
? ? ? ? ? ? order_target_percent(symbol=symbol, percent=buy_percent, order_type=OrderType_Market,
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? position_side=PositionSide_Long)
print(symbol,'以市價(jià)單開(kāi)多倉(cāng)至倉(cāng)位:', buy_percent)
else:
# 獲取過(guò)去5天的價(jià)格數(shù)據(jù),若連續(xù)上漲則為強(qiáng)勢(shì)股,權(quán)重+0.2;若連續(xù)下跌則為弱勢(shì)股,權(quán)重-0.2
recent_data = context.data(symbol=symbol, frequency='1d', count=5, fields='close')['close'].tolist()
ifall(np.diff(recent_data) >0):
buy_percent = context.stock300['weight'][symbol] * (context.ratio +0.2)
? ? ? ? ? ? ? ? order_target_percent(symbol=symbol, percent=buy_percent, order_type=OrderType_Market,
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? position_side=PositionSide_Long)
print('強(qiáng)勢(shì)股', symbol,'以市價(jià)單調(diào)多倉(cāng)至倉(cāng)位:', buy_percent)
elifall(np.diff(recent_data) <0):
buy_percent = context.stock300['weight'][symbol] * (context.ratio -0.2)
? ? ? ? ? ? ? ? order_target_percent(symbol=symbol, percent=buy_percent, order_type=OrderType_Market,
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? position_side=PositionSide_Long)
print('弱勢(shì)股', symbol,'以市價(jià)單調(diào)多倉(cāng)至倉(cāng)位:', buy_percent)
if__name__ =='__main__':
'''
? ? strategy_id策略ID,由系統(tǒng)生成
? ? filename文件名,請(qǐng)與本文件名保持一致
? ? mode實(shí)時(shí)模式:MODE_LIVE回測(cè)模式:MODE_BACKTEST
? ? token綁定計(jì)算機(jī)的ID,可在系統(tǒng)設(shè)置-密鑰管理中生成
? ? backtest_start_time回測(cè)開(kāi)始時(shí)間
? ? backtest_end_time回測(cè)結(jié)束時(shí)間
? ? backtest_adjust股票復(fù)權(quán)方式不復(fù)權(quán):ADJUST_NONE前復(fù)權(quán):ADJUST_PREV后復(fù)權(quán):ADJUST_POST
? ? backtest_initial_cash回測(cè)初始資金
? ? backtest_commission_ratio回測(cè)傭金比例
? ? backtest_slippage_ratio回測(cè)滑點(diǎn)比例
? ? '''
run(strategy_id='strategy_id',
filename='main.py',
? ? ? ? mode=MODE_BACKTEST,
token='token_id',
backtest_start_time='2017-07-01 08:50:00',
backtest_end_time='2017-10-01 17:00:00',
? ? ? ? backtest_adjust=ADJUST_PREV,
backtest_initial_cash=10000000,
backtest_commission_ratio=0.0001,
backtest_slippage_ratio=0.0001)
# coding=utf-8
from__future__importprint_function, absolute_import, unicode_literals
importnumpyasnp
fromgm.apiimport*
'''
本策略每隔1個(gè)月定時(shí)觸發(fā)計(jì)算SHSE.000910.SHSE.000909.SHSE.000911.SHSE.000912.SHSE.000913.SHSE.000914
(300工業(yè).300材料.300可選.300消費(fèi).300醫(yī)藥.300金融)這幾個(gè)行業(yè)指數(shù)過(guò)去
20個(gè)交易日的收益率并選取了收益率最高的指數(shù)的成份股獲取并獲取了他們的市值數(shù)據(jù)
隨后把倉(cāng)位調(diào)整至市值最大的5只股票上
回測(cè)數(shù)據(jù)為:SHSE.000910.SHSE.000909.SHSE.000911.SHSE.000912.SHSE.000913.SHSE.000914和他們的成份股
回測(cè)時(shí)間為:2017-07-01 08:00:00到2017-10-01 16:00:00
'''
definit(context):
# 每月第一個(gè)交易日的09:40 定時(shí)執(zhí)行algo任務(wù)
schedule(schedule_func=algo, date_rule='1m', time_rule='09:40:00')
# 用于篩選的行業(yè)指數(shù)
context.index = ['SHSE.000910','SHSE.000909','SHSE.000911','SHSE.000912','SHSE.000913','SHSE.000914']
# 用于統(tǒng)計(jì)數(shù)據(jù)的天數(shù)
context.date =20
# 最大下單資金比例
context.ratio =0.8
defalgo(context):
# 獲取當(dāng)天的日期
? ? today = context.now
# 獲取上一個(gè)交易日
last_day = get_previous_trading_date(exchange='SHSE', date=today)
? ? return_index = []
# 獲取并計(jì)算行業(yè)指數(shù)收益率
foriincontext.index:
return_index_his = history_n(symbol=i, frequency='1d', count=context.date, fields='close,bob',
fill_missing='Last', adjust=ADJUST_PREV, end_time=last_day, df=True)
return_index_his = return_index_his['close'].values
return_index.append(return_index_his[-1] / return_index_his[0] -1)
# 獲取指定數(shù)內(nèi)收益率表現(xiàn)最好的行業(yè)
? ? sector = context.index[np.argmax(return_index)]
print('最佳行業(yè)指數(shù)是: ', sector)
# 獲取最佳行業(yè)指數(shù)成份股
symbols = get_history_constituents(index=sector, start_date=last_day, end_date=last_day)[0]['constituents'].keys()
# 獲取當(dāng)天有交易的股票
? ? not_suspended_info = get_history_instruments(symbols=symbols, start_date=today, end_date=today)
not_suspended_symbols = [item['symbol']foriteminnot_suspended_infoifnotitem['is_suspended']]
# 獲取最佳行業(yè)指數(shù)成份股的市值喘沿，從大到小排序并選取市值最大的5只股票
fin = get_fundamentals(table='tq_sk_finindic', symbols=not_suspended_symbols, start_date=last_day,
end_date=last_day, limit=5, fields='NEGOTIABLEMV', order_by='-NEGOTIABLEMV', df=True)
fin.index = fin['symbol']
# 計(jì)算權(quán)重
percent =1.0/ len(fin.index) * context.ratio
# 獲取當(dāng)前所有倉(cāng)位
? ? positions = context.account().positions()
# 如標(biāo)的池有倉(cāng)位,平不在標(biāo)的池的倉(cāng)位
forpositioninpositions:
symbol = position['symbol']
ifsymbolnotinfin.index:
order_target_percent(symbol=symbol, percent=0, order_type=OrderType_Market,
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? position_side=PositionSide_Long)
print('市價(jià)單平不在標(biāo)的池的', symbol)
# 對(duì)標(biāo)的池進(jìn)行操作
forsymbolinfin.index:
? ? ? ? order_target_percent(symbol=symbol, percent=percent, order_type=OrderType_Market,
? ? ? ? ? ? ? ? ? ? ? ? ? ? position_side=PositionSide_Long)
print(symbol,'以市價(jià)單調(diào)整至倉(cāng)位', percent)
if__name__ =='__main__':
'''
? ? strategy_id策略ID,由系統(tǒng)生成
? ? filename文件名,請(qǐng)與本文件名保持一致
? ? mode實(shí)時(shí)模式:MODE_LIVE回測(cè)模式:MODE_BACKTEST
? ? token綁定計(jì)算機(jī)的ID,可在系統(tǒng)設(shè)置-密鑰管理中生成
? ? backtest_start_time回測(cè)開(kāi)始時(shí)間
? ? backtest_end_time回測(cè)結(jié)束時(shí)間
? ? backtest_adjust股票復(fù)權(quán)方式不復(fù)權(quán):ADJUST_NONE前復(fù)權(quán):ADJUST_PREV后復(fù)權(quán):ADJUST_POST
? ? backtest_initial_cash回測(cè)初始資金
? ? backtest_commission_ratio回測(cè)傭金比例
? ? backtest_slippage_ratio回測(cè)滑點(diǎn)比例
? ? '''
run(strategy_id='strategy_id',
filename='main.py',
? ? ? ? mode=MODE_BACKTEST,
token='token_id',
backtest_start_time='2017-07-01 08:00:00',
backtest_end_time='2017-10-01 16:00:00',
? ? ? ? backtest_adjust=ADJUST_PREV,
backtest_initial_cash=10000000,
backtest_commission_ratio=0.0001,
backtest_slippage_ratio=0.0001)
# coding=utf-8
from__future__importprint_function, absolute_import, unicode_literals
fromgm.apiimport*
'''
本策略通過(guò)獲取SHSE.000300滬深300的成份股數(shù)據(jù)并統(tǒng)計(jì)其30天內(nèi)
開(kāi)盤(pán)價(jià)大于前收盤(pán)價(jià)的天數(shù),并在該天數(shù)大于閾值10的時(shí)候加入股票池
隨后對(duì)不在股票池的股票平倉(cāng)并等權(quán)配置股票池的標(biāo)的,每次交易間隔1個(gè)月.
回測(cè)數(shù)據(jù)為:SHSE.000300在2015-01-15的成份股
回測(cè)時(shí)間為:2017-07-01 08:00:00到2017-10-01 16:00:00
'''
definit(context):
# 每月第一個(gè)交易日的09:40 定時(shí)執(zhí)行algo任務(wù)
schedule(schedule_func=algo, date_rule='1m', time_rule='09:40:00')
# context.count_bench累計(jì)天數(shù)闕值
context.count_bench =10
# 用于對(duì)比的天數(shù)
context.count =30
# 最大交易資金比例
context.ratio =0.8
defalgo(context):
# 獲取當(dāng)前時(shí)間
? ? now = context.now
# 獲取上一個(gè)交易日
last_day = get_previous_trading_date(exchange='SHSE', date=now)
# 獲取滬深300成份股
context.stock300 = get_history_constituents(index='SHSE.000300', start_date=last_day,
end_date=last_day)[0]['constituents'].keys()
# 獲取當(dāng)天有交易的股票
? ? not_suspended_info = get_history_instruments(symbols=context.stock300, start_date=now, end_date=now)
not_suspended_symbols = [item['symbol']foriteminnot_suspended_infoifnotitem['is_suspended']]
? ? trade_symbols = []
ifnotnot_suspended_symbols:
print('沒(méi)有當(dāng)日交易的待選股票')
return
forstockinnot_suspended_symbols:
recent_data = history_n(symbol=stock, frequency='1d', count=context.count, fields='pre_close,open',
fill_missing='Last', adjust=ADJUST_PREV, end_time=now, df=True)
diff = recent_data['open'] - recent_data['pre_close']
# 獲取累計(jì)天數(shù)超過(guò)闕值的標(biāo)的池.并剔除當(dāng)天沒(méi)有交易的股票
iflen(diff[diff >0]) >= context.count_bench:
? ? ? ? ? ? trade_symbols.append(stock)
print('本次股票池有股票數(shù)目: ', len(trade_symbols))
# 計(jì)算權(quán)重
percent =1.0/ len(trade_symbols) * context.ratio
# 獲取當(dāng)前所有倉(cāng)位
? ? positions = context.account().positions()
# 如標(biāo)的池有倉(cāng)位,平不在標(biāo)的池的倉(cāng)位
forpositioninpositions:
symbol = position['symbol']
ifsymbolnotintrade_symbols:
order_target_percent(symbol=symbol, percent=0, order_type=OrderType_Market,
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? position_side=PositionSide_Long)
print('市價(jià)單平不在標(biāo)的池的', symbol)
# 對(duì)標(biāo)的池進(jìn)行操作
forsymbolintrade_symbols:
? ? ? ? order_target_percent(symbol=symbol, percent=percent, order_type=OrderType_Market,
? ? ? ? ? ? ? ? ? ? ? ? ? ? position_side=PositionSide_Long)
print(symbol,'以市價(jià)單調(diào)整至權(quán)重', percent)
if__name__ =='__main__':
'''
? ? strategy_id策略ID,由系統(tǒng)生成
? ? filename文件名,請(qǐng)與本文件名保持一致
? ? mode實(shí)時(shí)模式:MODE_LIVE回測(cè)模式:MODE_BACKTEST
? ? token綁定計(jì)算機(jī)的ID,可在系統(tǒng)設(shè)置-密鑰管理中生成
? ? backtest_start_time回測(cè)開(kāi)始時(shí)間
? ? backtest_end_time回測(cè)結(jié)束時(shí)間
? ? backtest_adjust股票復(fù)權(quán)方式不復(fù)權(quán):ADJUST_NONE前復(fù)權(quán):ADJUST_PREV后復(fù)權(quán):ADJUST_POST
? ? backtest_initial_cash回測(cè)初始資金
? ? backtest_commission_ratio回測(cè)傭金比例
? ? backtest_slippage_ratio回測(cè)滑點(diǎn)比例
? ? '''
run(strategy_id='strategy_id',
filename='main.py',
? ? ? ? mode=MODE_BACKTEST,
token='token_id',
backtest_start_time='2017-07-01 08:00:00',
backtest_end_time='2017-10-01 16:00:00',
? ? ? ? backtest_adjust=ADJUST_PREV,
backtest_initial_cash=10000000,
backtest_commission_ratio=0.0001,
backtest_slippage_ratio=0.0001)
# coding=utf-8
from__future__importprint_function, absolute_import, unicode_literals
fromdatetimeimportdatetime
importnumpyasnp
fromgm.apiimport*
importsys
try:
fromsklearnimportsvm
except:
print('請(qǐng)安裝scikit-learn庫(kù)和帶mkl的numpy')
sys.exit(-1)
'''
本策略選取了七個(gè)特征變量組成了滑動(dòng)窗口長(zhǎng)度為15天的訓(xùn)練集,隨后訓(xùn)練了一個(gè)二分類(lèi)(上漲/下跌)的支持向量機(jī)模型.
若沒(méi)有倉(cāng)位則在每個(gè)星期一的時(shí)候輸入標(biāo)的股票近15個(gè)交易日的特征變量進(jìn)行預(yù)測(cè),并在預(yù)測(cè)結(jié)果為上漲的時(shí)候購(gòu)買(mǎi)標(biāo)的.
若已經(jīng)持有倉(cāng)位則在盈利大于10%的時(shí)候止盈,在星期五損失大于2%的時(shí)候止損.
特征變量為:1.收盤(pán)價(jià)/均值2.現(xiàn)量/均量3.最高價(jià)/均價(jià)4.最低價(jià)/均價(jià)5.現(xiàn)量6.區(qū)間收益率7.區(qū)間標(biāo)準(zhǔn)差
訓(xùn)練數(shù)據(jù)為:SHSE.600000浦發(fā)銀行,時(shí)間從2016-03-01到2017-06-30
回測(cè)時(shí)間為:2017-07-01 09:00:00到2017-10-01 09:00:00
'''
definit(context):
# 訂閱浦發(fā)銀行的分鐘bar行情
context.symbol ='SHSE.600000'
subscribe(symbols=context.symbol, frequency='60s')
start_date ='2016-03-01'# SVM訓(xùn)練起始時(shí)間
end_date ='2017-06-30'# SVM訓(xùn)練終止時(shí)間
# 用于記錄工作日
# 獲取目標(biāo)股票的daily歷史行情
recent_data = history(context.symbol, frequency='1d', start_time=start_date, end_time=end_date, fill_missing='last',
df=True)
days_value = recent_data['bob'].values
days_close = recent_data['close'].values
? ? days = []
# 獲取行情日期列表
print('準(zhǔn)備數(shù)據(jù)訓(xùn)練SVM')
foriinrange(len(days_value)):
days.append(str(days_value[i])[0:10])
? ? x_all = []
? ? y_all = []
forindexinrange(15, (len(days) -5)):
# 計(jì)算三星期共15個(gè)交易日相關(guān)數(shù)據(jù)
start_day = days[index -15]
? ? ? ? end_day = days[index]
data = history(context.symbol, frequency='1d', start_time=start_day, end_time=end_day, fill_missing='last',
df=True)
close = data['close'].values
max_x = data['high'].values
min_n = data['low'].values
amount = data['amount'].values
? ? ? ? volume = []
foriinrange(len(close)):
? ? ? ? ? ? volume_temp = amount[i] / close[i]
? ? ? ? ? ? volume.append(volume_temp)
close_mean = close[-1] / np.mean(close)# 收盤(pán)價(jià)/均值
volume_mean = volume[-1] / np.mean(volume)# 現(xiàn)量/均量
max_mean = max_x[-1] / np.mean(max_x)# 最高價(jià)/均價(jià)
min_mean = min_n[-1] / np.mean(min_n)# 最低價(jià)/均價(jià)
vol = volume[-1]# 現(xiàn)量
return_now = close[-1] / close[0]# 區(qū)間收益率
std = np.std(np.array(close), axis=0)# 區(qū)間標(biāo)準(zhǔn)差
# 將計(jì)算出的指標(biāo)添加到訓(xùn)練集X
# features用于存放因子
? ? ? ? features = [close_mean, volume_mean, max_mean, min_mean, vol, return_now, std]
? ? ? ? x_all.append(features)
# 準(zhǔn)備算法需要用到的數(shù)據(jù)
foriinrange(len(days_close) -20):
ifdays_close[i +20] > days_close[i +15]:
label =1
else:
label =0
? ? ? ? y_all.append(label)
x_train = x_all[:-1]
y_train = y_all[:-1]
# 訓(xùn)練SVM
context.clf = svm.SVC(C=1.0, kernel='rbf', degree=3, gamma='auto', coef0=0.0, shrinking=True, probability=False,
tol=0.001, cache_size=200, verbose=False, max_iter=-1,
decision_function_shape='ovr', random_state=None)
? ? context.clf.fit(x_train, y_train)
print('訓(xùn)練完成!')
defon_bar(context, bars):
bar = bars[0]
# 獲取當(dāng)前年月日
today = bar.bob.strftime('%Y-%m-%d')
# 獲取數(shù)據(jù)并計(jì)算相應(yīng)的因子
# 于星期一的09:31:00進(jìn)行操作
# 當(dāng)前bar的工作日
weekday = datetime.strptime(today,'%Y-%m-%d').isoweekday()
# 獲取模型相關(guān)的數(shù)據(jù)
# 獲取持倉(cāng)
? ? position = context.account().position(symbol=context.symbol, side=PositionSide_Long)
# 如果bar是新的星期一且沒(méi)有倉(cāng)位則開(kāi)始預(yù)測(cè)
ifnotpositionandweekday ==1:
# 獲取預(yù)測(cè)用的歷史數(shù)據(jù)
data = history_n(symbol=context.symbol, frequency='1d', end_time=today, count=15,
fill_missing='last', df=True)
close = data['close'].values
train_max_x = data['high'].values
train_min_n = data['low'].values
train_amount = data['amount'].values
? ? ? ? volume = []
foriinrange(len(close)):
? ? ? ? ? ? volume_temp = train_amount[i] / close[i]
? ? ? ? ? ? volume.append(volume_temp)
close_mean = close[-1] / np.mean(close)
volume_mean = volume[-1] / np.mean(volume)
max_mean = train_max_x[-1] / np.mean(train_max_x)
min_mean = train_min_n[-1] / np.mean(train_min_n)
vol = volume[-1]
return_now = close[-1] / close[0]
std = np.std(np.array(close), axis=0)
# 得到本次輸入模型的因子
? ? ? ? features = [close_mean, volume_mean, max_mean, min_mean, vol, return_now, std]
features = np.array(features).reshape(1,-1)
prediction = context.clf.predict(features)[0]
# 若預(yù)測(cè)值為上漲則開(kāi)倉(cāng)
ifprediction ==1:
# 獲取昨收盤(pán)價(jià)
context.price = close[-1]
# 把浦發(fā)銀行的倉(cāng)位調(diào)至95%
order_target_percent(symbol=context.symbol, percent=0.95, order_type=OrderType_Market,
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? position_side=PositionSide_Long)
print('SHSE.600000以市價(jià)單開(kāi)多倉(cāng)到倉(cāng)位0.95')
# 當(dāng)漲幅大于10%,平掉所有倉(cāng)位止盈
elifpositionandbar.close / context.price >=1.10:
? ? ? ? order_close_all()
print('SHSE.600000以市價(jià)單全平多倉(cāng)止盈')
# 當(dāng)時(shí)間為周五并且跌幅大于2%時(shí),平掉所有倉(cāng)位止損
elifpositionandbar.close / context.price <1.02andweekday ==5:
? ? ? ? order_close_all()
print('SHSE.600000以市價(jià)單全平多倉(cāng)止損')
if__name__ =='__main__':
'''
? ? strategy_id策略ID,由系統(tǒng)生成
? ? filename文件名,請(qǐng)與本文件名保持一致
? ? mode實(shí)時(shí)模式:MODE_LIVE回測(cè)模式:MODE_BACKTEST
? ? token綁定計(jì)算機(jī)的ID,可在系統(tǒng)設(shè)置-密鑰管理中生成
? ? backtest_start_time回測(cè)開(kāi)始時(shí)間
? ? backtest_end_time回測(cè)結(jié)束時(shí)間
? ? backtest_adjust股票復(fù)權(quán)方式不復(fù)權(quán):ADJUST_NONE前復(fù)權(quán):ADJUST_PREV后復(fù)權(quán):ADJUST_POST
? ? backtest_initial_cash回測(cè)初始資金
? ? backtest_commission_ratio回測(cè)傭金比例
? ? backtest_slippage_ratio回測(cè)滑點(diǎn)比例
? ? '''
run(strategy_id='strategy_id',
filename='main.py',
? ? ? ? mode=MODE_BACKTEST,
token='token_id',
backtest_start_time='2017-07-01 09:00:00',
backtest_end_time='2017-10-01 09:00:00',
? ? ? ? backtest_adjust=ADJUST_PREV,
backtest_initial_cash=10000000,
backtest_commission_ratio=0.0001,
backtest_slippage_ratio=0.0001)
# coding=utf-8
from__future__importprint_function, absolute_import, unicode_literals
fromgm.apiimport*
importnumpyasnp
definit(context):
#獲得N日股票交易數(shù)據(jù)
context.N=5
#選擇一對(duì)股票
context.stock=['SZSE.000651','SZSE.000333']
# 每個(gè)交易日的09:40 定時(shí)執(zhí)行algo任務(wù)
schedule(schedule_func=algo, date_rule='1d', time_rule='09:40:00')
defalgo(context):
# 獲取上一個(gè)交易日的日期
last_day = get_previous_trading_date(exchange='SHSE', date=context.now)
# 獲取當(dāng)天有交易的股票闸度，似乎無(wú)法同時(shí)獲得兩只股票的數(shù)據(jù)，所以只能麻煩一點(diǎn)
not_suspended = get_history_instruments(symbols=context.stock[0], start_date=last_day, end_date=last_day)
a = len([item['symbol']foriteminnot_suspendedifnotitem['is_suspended']])
not_suspended = get_history_instruments(symbols=context.stock[1], start_date=last_day,end_date=last_day)
b = len([item['symbol']foriteminnot_suspendedifnotitem['is_suspended']])
#如果有一支停牌蚜印，就跳過(guò)
ifa+b<2:
return
#獲得交易數(shù)據(jù)
prices1 = history_n(symbol=context.stock[0], frequency='1d', count=context.N, end_time=last_day, fields='close',
skip_suspended=True,
fill_missing=None, adjust=ADJUST_PREV, adjust_end_time='', df=True)
prices2=history_n(symbol=context.stock[1], frequency='1d', count=context.N, end_time=last_day, fields='close',
skip_suspended=True,
fill_missing=None, adjust=ADJUST_PREV, adjust_end_time='', df=True)
p1=list(prices1['close'])
p2=list(prices2['close'])
spread = np.array(p1[:-1]) - np.array(p2[:-1])
# 計(jì)算布林帶的上下軌
up = np.mean(spread) +2* np.std(spread)
down = np.mean(spread) -2* np.std(spread)
# 計(jì)算最新價(jià)差
spread_now = p1[-1] - p2[-1]
# 無(wú)交易時(shí)若價(jià)差上(下)穿布林帶上(下)軌則做空(多)價(jià)差
position_s1_long = context.account().position(symbol=context.stock[0], side=PositionSide_Long)
position_s2_long = context.account().position(symbol=context.stock[1], side=PositionSide_Long)
ifnotposition_s1_longandnotposition_s2_long:
ifspread_now > up:
order_target_percent(symbol=context.stock[1], percent=0.5, order_type=OrderType_Market,
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? position_side=PositionSide_Long)
ifspread_now < down:
order_target_percent(symbol=context.stock[0], percent=0.5, order_type=OrderType_Market,
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? position_side=PositionSide_Long)
# 價(jià)差回歸時(shí)平倉(cāng)
elifposition_s2_long:
ifspread_now <= up:
? ? ? ? ? ? order_close_all()
elifposition_s1_long:
ifspread_now >= down:
? ? ? ? ? ? order_close_all()
if__name__ =='__main__':
'''
? ? strategy_id策略ID,由系統(tǒng)生成
? ? filename文件名,請(qǐng)與本文件名保持一致
? ? mode實(shí)時(shí)模式:MODE_LIVE回測(cè)模式:MODE_BACKTEST
? ? token綁定計(jì)算機(jī)的ID,可在系統(tǒng)設(shè)置-密鑰管理中生成
? ? backtest_start_time回測(cè)開(kāi)始時(shí)間
? ? backtest_end_time回測(cè)結(jié)束時(shí)間
? ? backtest_adjust股票復(fù)權(quán)方式不復(fù)權(quán):ADJUST_NONE前復(fù)權(quán):ADJUST_PREV后復(fù)權(quán):ADJUST_POST
? ? backtest_initial_cash回測(cè)初始資金
? ? backtest_commission_ratio回測(cè)傭金比例
? ? backtest_slippage_ratio回測(cè)滑點(diǎn)比例
? ? '''
run(strategy_id='73bb5bf2-a536-11e8-bd52-9cd21ef04ea9',
filename='配對(duì)交易.py',
? ? ? ? mode=MODE_BACKTEST,
token='c395247a76e8a5caeee699d668d6f550213bc418',
backtest_start_time='2014-01-01 08:00:00',
backtest_end_time='2018-08-01 16:00:00',
? ? ? ? backtest_adjust=ADJUST_PREV,
backtest_initial_cash=10000000,
backtest_commission_ratio=0.0001,
backtest_slippage_ratio=0.0001)
# coding=utf-8
from__future__importprint_function, absolute_import, unicode_literals
fromgm.apiimport*
fromsklearnimportpreprocessing
'''
策略思路：
1莺禁、公司的資產(chǎn)負(fù)債率小于等于 25%
2、公司每股凈現(xiàn)金大于 0
3窄赋、當(dāng)前股價(jià)與每股自由現(xiàn)金流量比小于 10(市現(xiàn)率)
4哟冬、在所有股票中取市盈率排倒數(shù)30%的股票(首先PE必須大于0)
5、PEG=市盈率/凈利潤(rùn)增長(zhǎng)率<0.5
回測(cè)數(shù)據(jù):SHSE.000906的成份股
回測(cè)時(shí)間:2016-01-01 08:00:00到2018-01-01 16:00:00
'''
definit(context):
# 每月第一個(gè)交易日的09:40 定時(shí)執(zhí)行algo任務(wù)
schedule(schedule_func=algo, date_rule='1m', time_rule='09:40:00')
defalgo(context):
# 獲取上一個(gè)交易日的日期
last_day = get_previous_trading_date(exchange='SHSE', date=context.now)
# 獲取滬深300成份股
stock800 = get_history_constituents(index='SHSE.000906', start_date=last_day,
end_date=last_day)[0]['constituents'].keys()
# 獲取當(dāng)天有交易的股票
? ? not_suspended = get_history_instruments(symbols=stock800, start_date=last_day, end_date=last_day)
not_suspended = [item['symbol']foriteminnot_suspendedifnotitem['is_suspended']]
df = get_fundamentals(table='deriv_finance_indicator', symbols=not_suspended, start_date=last_day, end_date=last_day,
fields='ASSLIABRT,NCFPS,NPGRT', df=True)
fin=get_fundamentals(table='trading_derivative_indicator', symbols=not_suspended, start_date=last_day, end_date=last_day,
fields='PCLFY,PELFY', df=True)
df['PCLFY']=fin['PCLFY']
df['PELFY'] = fin['PELFY']
# 除去空值
? ? df = df.dropna()
df['PEG']=df['PELFY']/df['NPGRT']
? ? df.index=df.symbol
deldf['symbol'],df['pub_date'],df['end_date']
? ? print(df)
# 選出PEG小于0.5的部分
df = df[df['PEG'] <0.5]
# 選出債務(wù)總資產(chǎn)比小于0.25的部分
df = df[df["ASSLIABRT"] <25]
# 選出每股凈現(xiàn)金大于 0的部分
df = df[df["NCFPS"] >0]
# 選出市盈率大于零的部分
df = df[df['PELFY'] >0]
# 選出市現(xiàn)率小于10的部分
df = df[df['PCLFY'] <10]
? ? print(df)
# 剔除市盈率較高的股票（即剔除3分位數(shù)以后的股票）
iflen(df)<4:
? ? ? ? symbols_pool = list(df.index)
else:
df = df[(df['PELFY'] < df['PELFY'].quantile(0.3))]
? ? ? ? symbols_pool = list(df.index)
? ? print(symbols_pool)
? ? order_close_all()
? ? long=len(symbols_pool)
iflong==0:
return
# 獲取股票的權(quán)重
percent =1/ long
# 買(mǎi)在標(biāo)的池中的股票
forsymbolinsymbols_pool:
? ? ? ? order_target_percent(symbol=symbol, percent=percent, order_type=OrderType_Market,
? ? ? ? ? ? ? ? ? ? ? ? ? ? position_side=PositionSide_Long)
#print(symbol, '以市價(jià)單調(diào)多倉(cāng)到倉(cāng)位', percent)
if__name__ =='__main__':
'''
? ? strategy_id策略ID,由系統(tǒng)生成
? ? filename文件名,請(qǐng)與本文件名保持一致
? ? mode實(shí)時(shí)模式:MODE_LIVE回測(cè)模式:MODE_BACKTEST
? ? token綁定計(jì)算機(jī)的ID,可在系統(tǒng)設(shè)置-密鑰管理中生成
? ? backtest_start_time回測(cè)開(kāi)始時(shí)間
? ? backtest_end_time回測(cè)結(jié)束時(shí)間
? ? backtest_adjust股票復(fù)權(quán)方式不復(fù)權(quán):ADJUST_NONE前復(fù)權(quán):ADJUST_PREV后復(fù)權(quán):ADJUST_POST
? ? backtest_initial_cash回測(cè)初始資金
? ? backtest_commission_ratio回測(cè)傭金比例
? ? backtest_slippage_ratio回測(cè)滑點(diǎn)比例
? ? '''
run(strategy_id='73bb5bf2-a536-11e8-bd52-9cd21ef04ea9',
filename='GARP.py',
? ? ? ? mode=MODE_BACKTEST,
token='c395247a76e8a5caeee699d668d6f550213bc418',
backtest_start_time='2016-01-01 08:00:00',
backtest_end_time='2018-01-01 16:00:00',
? ? ? ? backtest_adjust=ADJUST_PREV,
backtest_initial_cash=10000000,
backtest_commission_ratio=0.0001,
backtest_slippage_ratio=0.0001)
# coding=utf-8
from__future__importprint_function, absolute_import, unicode_literals
fromgm.apiimport*
'''
本策略每隔1個(gè)月定時(shí)觸發(fā)計(jì)算SHSE.000300成份股的過(guò)去的EV/EBITDA并選取EV/EBITDA大于0的股票
隨后平掉排名EV/EBITDA不在最小的30的股票持倉(cāng)并等權(quán)購(gòu)買(mǎi)EV/EBITDA最小排名在前30的股票
并用相應(yīng)的CFFEX.IF對(duì)應(yīng)的真實(shí)合約等額對(duì)沖
回測(cè)數(shù)據(jù)為:SHSE.000300和他們的成份股和CFFEX.IF對(duì)應(yīng)的真實(shí)合約
回測(cè)時(shí)間為:2017-07-01 08:00:00到2017-10-01 16:00:00
'''
definit(context):
# 每月第一個(gè)交易日09:40:00的定時(shí)執(zhí)行algo任務(wù)
schedule(schedule_func=algo, date_rule='1m', time_rule='09:40:00')
# 設(shè)置開(kāi)倉(cāng)在股票和期貨的資金百分比(期貨在后面自動(dòng)進(jìn)行杠桿相關(guān)的調(diào)整)
context.percentage_stock =0.4
context.percentage_futures =0.4
defalgo(context):
# 獲取當(dāng)前時(shí)刻
? ? now = context.now
# 獲取上一個(gè)交易日
last_day = get_previous_trading_date(exchange='SHSE', date=now)
# 獲取滬深300成份股
stock300 = get_history_constituents(index='SHSE.000300', start_date=last_day,
end_date=last_day)[0]['constituents'].keys()
# 獲取上一個(gè)工作日的CFFEX.IF對(duì)應(yīng)的合約
index_futures = get_continuous_contracts(csymbol='CFFEX.IF', start_date=last_day, end_date=last_day)[-1]['symbol']
# 獲取當(dāng)天有交易的股票
? ? not_suspended_info = get_history_instruments(symbols=stock300, start_date=now, end_date=now)
not_suspended_symbols = [item['symbol']foriteminnot_suspended_infoifnotitem['is_suspended']]
# 獲取成份股EV/EBITDA大于0并為最小的30個(gè)
fin = get_fundamentals(table='tq_sk_finindic', symbols=not_suspended_symbols,
start_date=now, end_date=now, fields='EVEBITDA',
filter='EVEBITDA>0', order_by='EVEBITDA', limit=30, df=True)
? ? fin.index = fin.symbol
# 獲取當(dāng)前倉(cāng)位
? ? positions = context.account().positions()
# 平不在標(biāo)的池或不為當(dāng)前股指期貨主力合約對(duì)應(yīng)真實(shí)合約的標(biāo)的
forpositioninpositions:
symbol = position['symbol']
sec_type = get_instrumentinfos(symbols=symbol)[0]['sec_type']
# 若類(lèi)型為期貨且不在標(biāo)的池則平倉(cāng)
ifsec_type == SEC_TYPE_FUTUREandsymbol != index_futures:
order_target_percent(symbol=symbol, percent=0, order_type=OrderType_Market,
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? position_side=PositionSide_Short)
print('市價(jià)單平不在標(biāo)的池的', symbol)
elifsymbolnotinfin.index:
order_target_percent(symbol=symbol, percent=0, order_type=OrderType_Market,
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? position_side=PositionSide_Long)
print('市價(jià)單平不在標(biāo)的池的', symbol)
# 獲取股票的權(quán)重
? ? percent = context.percentage_stock / len(fin.index)
# 買(mǎi)在標(biāo)的池中的股票
forsymbolinfin.index:
? ? ? ? order_target_percent(symbol=symbol, percent=percent, order_type=OrderType_Market,
? ? ? ? ? ? ? ? ? ? ? ? ? ? position_side=PositionSide_Long)
print(symbol,'以市價(jià)單調(diào)多倉(cāng)到倉(cāng)位', percent)
# 獲取股指期貨的保證金比率
ratio = get_history_instruments(symbols=index_futures, start_date=last_day, end_date=last_day)[0]['margin_ratio']
# 更新股指期貨的權(quán)重
? ? percent = context.percentage_futures * ratio
# 買(mǎi)入股指期貨對(duì)沖
? ? order_target_percent(symbol=index_futures, percent=percent, order_type=OrderType_Market,
? ? ? ? ? ? ? ? ? ? ? ? position_side=PositionSide_Short)
print(index_futures,'以市價(jià)單調(diào)空倉(cāng)到倉(cāng)位', percent)
if__name__ =='__main__':
'''
? ? strategy_id策略ID,由系統(tǒng)生成
? ? filename文件名,請(qǐng)與本文件名保持一致
? ? mode實(shí)時(shí)模式:MODE_LIVE回測(cè)模式:MODE_BACKTEST
? ? token綁定計(jì)算機(jī)的ID,可在系統(tǒng)設(shè)置-密鑰管理中生成
? ? backtest_start_time回測(cè)開(kāi)始時(shí)間
? ? backtest_end_time回測(cè)結(jié)束時(shí)間
? ? backtest_adjust股票復(fù)權(quán)方式不復(fù)權(quán):ADJUST_NONE前復(fù)權(quán):ADJUST_PREV后復(fù)權(quán):ADJUST_POST
? ? backtest_initial_cash回測(cè)初始資金
? ? backtest_commission_ratio回測(cè)傭金比例
? ? backtest_slippage_ratio回測(cè)滑點(diǎn)比例
? ? '''
run(strategy_id='strategy_id',
filename='main.py',
? ? ? ? mode=MODE_BACKTEST,
token='token_id',
backtest_start_time='2017-07-01 08:00:00',
backtest_end_time='2017-10-01 16:00:00',
? ? ? ? backtest_adjust=ADJUST_PREV,
backtest_initial_cash=10000000,
backtest_commission_ratio=0.0001,
backtest_slippage_ratio=0.0001)
鏈接下文：經(jīng)典的股票/期貨量化交易策略忆绰，拿來(lái)即用的Python策略源碼（2）
