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因子计算代码更新到#50; #24号因子实现策略化

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Harvey-Sun
2017-02-28 17:55:16 +08:00
parent a74dc29469
commit 26a6042b43
2 changed files with 183 additions and 0 deletions
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#024/alpha#024.py
+125
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@@ -0,0 +1,125 @@
# -*- coding:utf-8 -*-
from CloudQuant import MiniSimulator
import numpy as np
import pandas as pd
username = 'Harvey_Sun'
password = 'P948894dgmcsy'
Strategy_Name = 'WQ_alpha#024_10_100'
INIT_CAP = 100000000
START_DATE = '20130101'
END_DATE = '20161231'
Fee_Rate = 0.001
program_path = 'C:/cStrategy/'
buy_number = 100
period = 10
first = False
def initial(sdk):
sdk.prepareData(['LZ_GPA_QUOTE_TCLOSE', 'LZ_GPA_QUOTE_TVOLUME',
'LZ_GPA_CMFTR_CUM_FACTOR', 'LZ_GPA_SLCIND_STOP_FLAG'])
stock_pool = []
sdk.setGlobal('stock_pool', stock_pool)
step = period
sdk.setGlobal('step', step)
def init_per_day(sdk):
step = sdk.getGlobal('step')
if step == period:
stock_list = sdk.getStockList()
close = pd.DataFrame(sdk.getFieldData('LZ_GPA_QUOTE_TCLOSE')[-200:], columns=stock_list)
adj_f = pd.DataFrame(sdk.getFieldData('LZ_GPA_CMFTR_CUM_FACTOR')[-200:], columns=stock_list)
stop = pd.DataFrame(sdk.getFieldData('LZ_GPA_SLCIND_STOP_FLAG')[-4:], columns=stock_list)
adj_close = close * adj_f
# 计算昨天的factor
factor = -1 * adj_close.diff(3).iloc[-1]
cond_1 = (adj_close.rolling(window=100).mean().diff(100) / adj_close.shift(100)).iloc[-1] <= 0.05
sub = -1 * (adj_close - adj_close.rolling(window=100).min()).iloc[-1]
factor[cond_1] = sub
factor[stop.notnull().any()] = np.nan # 剔除停牌的股票
factor.sort_values(ascending=first, inplace=True)
stock_pool = factor.index[:buy_number] # 选出的股票
position = sdk.getPositions()
position_dict = dict([i.code, i.optPosition] for i in position)
stock_to_buy = set(stock_pool) - set(position_dict.keys())
stock_to_sell = set(position_dict.keys()) - set(stock_pool)
sdk.setGlobal('position_dict', position_dict)
sdk.setGlobal('stock_to_buy', stock_to_buy)
sdk.setGlobal('stock_to_sell', stock_to_sell)
else:
pass
def strategy(sdk):
step = sdk.getGlobal('step')
if step == period:
step = 0
today = sdk.getNowDate()
sdk.sdklog(today,'====================')
position_dict = sdk.getGlobal('position_dict')
stock_to_buy = sdk.getGlobal('stock_to_buy')
stock_to_sell = sdk.getGlobal('stock_to_sell')
quotes = sdk.getQuotes(list(stock_to_buy | stock_to_sell))
sell_orders = []
for stock in stock_to_sell:
if stock in quotes.keys():
price = quotes[stock].open
volume = position_dict[stock]
order = [stock, price, volume, -1]
sell_orders.append(order)
if sell_orders:
sdk.makeOrders(sell_orders)
sdk.sdklog('===sell orders===')
sdk.sdklog(np.array(sell_orders))
available_cash = sdk.getAccountInfo().availableCash
available_cash_one_stock = available_cash / len(stock_to_buy)
buy_orders = []
for stock in stock_to_buy:
if stock in quotes.keys():
price = quotes[stock].open
volume = int(available_cash_one_stock / (price * 100)) * 100
if volume > 0:
order = [stock, price, volume, 1]
buy_orders.append(order)
if buy_orders:
sdk.makeOrders(buy_orders)
sdk.sdklog('===buy orders===')
sdk.sdklog(np.array(buy_orders))
else:
step += 1
sdk.setGlobal('step', step)
config = {
'username': username,
'password': password,
'initCapital': INIT_CAP,
'startDate': START_DATE,
'endDate': END_DATE,
'strategy': strategy,
'initial': initial,
'preparePerDay': init_per_day,
'feeRate': Fee_Rate,
'strategyName': Strategy_Name,
'logfile': '%s.log' % Strategy_Name,
'rootpath': program_path,
'executeMode': 'D',
'feeLimit': 5,
'cycle': 10,
'dealByVolume': True,
'allowForTodayFactors': ['LZ_GPA_SLCIND_STOP_FLAG']
}
if __name__ == "__main__":
# 在线运行所需代码
import os
config['strategyID'] = os.path.splitext(os.path.split(__file__)[1])[0]
MiniSimulator(**config).run()
WQ_Alphas_Analysis.py
+58
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@@ -86,6 +86,8 @@ def factor_cal(data, factor_id, no_zdt=False, no_st=False, no_new=False):
volume = data['volume']
stop = data['stop']
st = data['st']
fcf = data['fcf']
asset_debt = data['asset_debt']
price_adj_f = data['price_adj_f']
adj_OPEN = OPEN * price_adj_f
adj_close = close * price_adj_f
@@ -330,6 +332,54 @@ def factor_cal(data, factor_id, no_zdt=False, no_st=False, no_new=False):
elif factor_id == '041':
factor = ((high * low) ** 0.5 - (value / volume) * 10)
factor[stop.notnull()] = np.nan
elif factor_id == '042':
vwap = value / volume
factor = (vwap - close).rank(axis=1, pct=True) / (vwap + close).rank(axis=1, pct=True)
factor[stop.notnull()] = np.nan
elif factor_id == '043':
part_1 = rolling_rank(adj_volume / adj_volume.rolling(window=20).mean(), 20)
part_2 = rolling_rank(-1 * adj_close.diff(7), 8)
factor = part_1 * part_2
factor[rolling_stop(stop, 20)] = np.nan
elif factor_id == '044':
factor = -1 * adj_high.rolling(window=5).corr(adj_volume.rank(axis=1, pct=True))
factor[rolling_stop(stop, 5)] = np.nan
elif factor_id == '045':
part_1 = adj_close.shift(5).rolling(window=20).mean().rank(axis=1, pct=True)
part_2 = adj_close.rolling(window=2).corr(adj_volume)
temp1 = adj_close.rolling(window=5).sum()
temp2 = adj_close.rolling(window=20).sum()
part_3 = temp1.rolling(window=2).corr(temp2).rank(axis=1, pct=True)
factor = -1 * part_1 * part_2 * part_3
factor[rolling_stop(stop, 20)] = np.nan
elif factor_id == '046':
factor = -1 * adj_close.diff(1)
temp1 = (adj_close.shift(20) - adj_close.shift(10)) / 10
temp2 = (adj_close.shift(10) - adj_close) / 10
cond_1 = temp1 - temp2 > 0.25
cond_2 = temp1 - temp2 < 0
factor[cond_2] = 1
factor[cond_1] = -1
factor[rolling_stop(stop, 20)] = np.nan
elif factor_id == '047':
part_1 = (1 / adj_close).rank(axis=1, pct=True) * adj_volume / adj_volume.rolling(window=20).mean()
part_2 = adj_high * (adj_high - adj_close).rank(axis=1, pct=True) / adj_high.rolling(window=5).mean()
vwap = value / adj_volume
part_3 = vwap.diff(5).rank(axis=1, pct=True)
factor = part_1 * part_2 * part_3
factor[rolling_stop(stop, 20)] = np.nan
elif factor_id == '049':
factor = -1 * adj_close.diff()
temp1 = (adj_close.shift(20) - adj_close.shift(10)) / 10
temp2 = (adj_close.shift(10) - adj_close) / 10
factor[temp1 - temp2 < 0.1] = 1
factor[rolling_stop(stop, 20)] = np.nan
elif factor_id == '050':
volume_rank = adj_volume.rank(axis=1, pct=True)
vwap_rank = (value / adj_volume).rank(axis=1, pct=True)
corr_ = volume_rank.rolling(window=5).corr(vwap_rank)
factor = -1 * rolling_rank(corr_.rank(axis=1, pct=True), 5)
factor[rolling_stop(stop, 5)] = np.nan
elif factor_id == '055':
numerator = adj_close - adj_low.rolling(window=12).min()
denominator = adj_high.rolling(window=12).max() - adj_low.rolling(window=12).min()
@@ -357,6 +407,10 @@ def factor_cal(data, factor_id, no_zdt=False, no_st=False, no_new=False):
vol.index.name = t
factor[t] = vol
factor = factor.T
elif factor_id == 'PeterLynch':
fcf[fcf < 0] = np.nan
asset_debt[asset_debt < 0] = np.nan
factor = (close / fcf).rank(axis=1, pct=True) * asset_debt.rank(axis=1, pct=True)
else:
print 'factor id is wrong'
# 将涨跌停股票的因子设为na
@@ -510,6 +564,8 @@ volume = import_data('LZ_GPA_QUOTE_TVOLUME')
stop = import_data('LZ_GPA_SLCIND_STOP_FLAG')
price_adj_f = import_data('LZ_GPA_CMFTR_CUM_FACTOR')
st = import_data('LZ_GPA_SLCIND_ST_FLAG')
fcf = import_data('LZ_GPA_FIN_IND_FCFFPS')
asset_debt = import_data('LZ_GPA_FIN_IND_DEBTTOASSETS')
data = {'high': high,
'low': low,
@@ -519,6 +575,8 @@ data = {'high': high,
'volume': volume,
'stop': stop,
'st': st,
'fcf': fcf,
'asset_debt': asset_debt,
'price_adj_f': price_adj_f}