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| from youngquant.api import *
from youngquant import exec_strategy
from yqdata.services.factor import *
import yqdata.services as yq
import pandas as pd
import numpy as np
import datetime
import statsmodels.api as sm
import warnings
import talib
warnings.filterwarnings('ignore')
config = {
"base": {
"start_date": "2021-01-01",
"end_date": "2021-06-30",
"frequency": "1d",
"benchmark": "000300.XSHG",
"accounts": {
"stock": 1000000
}
},
"extra": {
"log_level": "error",
},
"mod": {
"sys_analyser": {
"enabled": True,
"plot": True
},
"mongodb": {
"enabled": True,
}
}
}
def find_order_stocks(context, bar_dict):
'''
根据选股思路确定本月想下单的股票池，存入context.yq_order_stocks
'''
df_tscodes = yq.concept.sw_detail(index_code='801156.SI')
ts_codes = df_tscodes['con_code']
factors_dict = {}
start_time = (context.now - datetime.timedelta(days=context.observation)).strftime("%Y%m%d")
end_time = context.now.strftime("%Y%m%d")
print('start_time = ',start_time)
print('end_time = ',end_time)
for ts_code in ts_codes:
yq_code = ts_code.replace('SH', 'XSHG').replace('SZ', 'XSHE')
if instruments(yq_code).days_from_listed() > context.observation and not any(is_suspended(yq_code, context.observation)):
Size = yq.basic.daily_basic(ts_code=ts_code, start_date=start_time, end_date=end_time, fields='total_mv')
Log_Size = np.log(Size['total_mv'].mean())
query_body = {
"_source": ["ts_code", "turnover_rate_f", "trade_date"],
"query": {
"bool": {
"must": [
{"match_phrase": {"ts_code": ts_code}},
{"exists": {"field": "turnover_rate_f"}},
{"range": {"trade_date": {"gte": start_time, "lte": end_time}}}
]
}
},
"size": 10000
}
df = yq.common.query(index='daily_basic', body=query_body)
Liquidity = np.log(df["turnover_rate_f"].sum())
dff = get_finance_factor(ts_code=ts_code,start_date=start_time, end_date=end_time,factors='debt_to_asset_ratio_ttm,book_leverage_ttm,market_leverage_ttm')
if not dff.empty:
dff.fillna(0, inplace=True)
DTOA_mean = dff['debt_to_asset_ratio_ttm'].mean()
BLEV_mean = dff['book_leverage_ttm'].mean()
MLEV_mean = dff['market_leverage_ttm'].mean()
Leverage = 0.35*DTOA_mean+0.27*BLEV_mean+0.38*MLEV_mean
else:
Leverage = 0
df_btop = yq.basic.daily_basic(ts_code=ts_code, start_date=start_time, end_date=end_time, fields='ts_code,pb')
df_btop['btop']= 1/df_btop['pb']
Book_to_price =df_btop['btop'].mean()
df = pd.DataFrame()
df_var = yq.basic.daily_basic(ts_code=ts_code, start_date=start_time, end_date=end_time, fields='close')
df['daily_return'] = df_var['close'].pct_change()
df = df.drop(df.index[0])
halflife = 12
ewma = df['daily_return'].ewm(halflife=halflife).mean()
Residual_Volatility = np.sum(((df['daily_return'] - ewma) ** 2) * np.exp(-np.arange(len(df)) / halflife)) / np.sum(np.exp(-np.arange(len(df)) / halflife))
factors_dict[ts_code] = {'Size':Log_Size,'Liquidity':Liquidity,'Book_to_price': Book_to_price,'Leverage': Leverage,'Volatility': Residual_Volatility}
factors_df = pd.DataFrame(factors_dict).T
factors_df['Size_rank'] = factors_df['Size'].rank(ascending=False)
factors_df['Leverage_rank'] = factors_df['Leverage'].rank()
factors_df['Liquidity_rank'] = factors_df['Liquidity'].rank(ascending=False)
factors_df['Book_to_price_rank'] = factors_df['Book_to_price'].rank(ascending=False)
factors_df['Volatility_rank'] = factors_df['Volatility'].rank(ascending=False)
topM, topN = context.topM, context.topN
alternative_stocks = factors_df.query('Volatility_rank < @topM & Size_rank < @topM & Book_to_price_rank < @topM & Liquidity_rank < @topM & Leverage_rank < @topM' )
alternative_stocks['total_rank'] = alternative_stocks[['Size_rank', 'Book_to_price_rank', 'Liquidity_rank','Leverage_rank','Volatility_rank']].mean(axis=1).rank()
order_stocks = alternative_stocks.query('total_rank < @topN').index
context.yq_order_stocks = [stock.replace('SH','XSHG').replace('SZ','XSHE') for stock in order_stocks]
SHORTPERIOD = 12
LONGPERIOD = 26
SMOOTHPERIOD = 9
OBSERVATION = 200
for stock in context.yq_order_stocks:
pricefilter = history_bars(stock,OBSERVATION,'1d','close',adjust_type='pre')
macd, signal, hist = talib.MACD(pricefilter, SHORTPERIOD, LONGPERIOD, SMOOTHPERIOD)
if macd[-1] - signal[-1] < 0:
context.yq_order_stocks.remove(stock)
def sink_stock(context, bar_dict):
'''卖出本月未入选的股票，买入新入选的股票'''
stock_to_sell = set(context.portfolio.positions) - set(context.yq_order_stocks)
target_percent = 1 / len(context.yq_order_stocks)
for stock in stock_to_sell:
order_target_percent(stock, 0)
for stock in context.yq_order_stocks:
order_target_percent(stock, target_percent)
def price_in(context,bar_dict):
'''用上一交易日的收盘价近似本月第一个交易日的开盘价，即成交价'''
data0=[]
for stock in context.yq_order_stocks:
open0=history_bars(stock,1,'1d','close',adjust_type='pre')
data0.append(open0)
context.data0=data0
def stop_losing(context,bar_dict):
'''止损'''
for i in range(len(context.yq_order_stocks)):
lossdata=history_bars(context.yq_order_stocks[i],1,'1d','close',adjust_type='pre')
if lossdata < context.data0[i]*0.7:
order_target_percent(context.yq_order_stocks[i], 0)
def initialize(context):
context.observation = 30
context.topM = 200
context.topN = 10
scheduler.run_monthly(find_order_stocks, tradingday=1, time_rule='before_trading')
scheduler.run_monthly(sink_stock, tradingday=1, time_rule=market_open(hour=0))
scheduler.run_monthly(price_in,tradingday=1)
scheduler.run_daily(stop_losing)
def handle_data(context, bar_dict):
pass
exec_strategy(initialize=initialize, handle_data=handle_data, config=config)
|
求求富哥打赏~QwQ
