modify factor cash flow

.gitignore

@@ -107,3 +107,4 @@ venv.bak/
 .idea/
 /ultron/
 /client_bak.py
+/client_.py

client_.py

-import pdb
-from alphamind.api import *
-from PyFin.api import *
-from PyFin.api import makeSchedule
-from sqlalchemy import create_engine, select, and_, or_
-from sqlalchemy.pool import NullPool
-from factors.models import Alpha191
-import pandas as pd
-import time
-import datetime
-import json
-import sys
-from factors import analysis
-from ultron.cluster.invoke.cache_data import cache_data
-from ultron.utilities.short_uuid import unique_machine,decode
-    
-def fetch_factor(engine191, factor_names, start_date, end_date):
-    db_columns = []
-    db_columns.append(Alpha191.trade_date)
-    db_columns.append(Alpha191.code)
-    for factor_name in factor_names:
-        db_columns.append(Alpha191.__dict__[factor_name])
-    query = select(db_columns).where(
-        and_(Alpha191.trade_date >= start_date, Alpha191.trade_date <= end_date, ))
-    return pd.read_sql(query, engine191)
-
-def factor_combination(engine, factors, universe_name_list, start_date, end_date, freq):
-    universe = None
-    for name in universe_name_list:
-        if universe is None:
-            universe = Universe(name)
-        else:
-            universe += Universe(name)
-    dates = makeSchedule(start_date, end_date, freq, calendar='china.sse')
-    factor_negMkt = engine.fetch_factor_range(universe, "negMarketValue", dates=dates)
-    risk_cov, risk_factors = engine.fetch_risk_model_range(universe, dates=dates)
-    dx_returns = engine.fetch_dx_return_range(universe, dates=dates, horizon=map_freq(freq))
-
-    # data combination
-    total_data = pd.merge(factors, risk_factors, on=['trade_date', 'code'])
-    total_data = pd.merge(total_data, factor_negMkt, on=['trade_date', 'code'])
-    total_data = pd.merge(total_data, dx_returns, on=['trade_date', 'code'])
-    
-    industry_category = engine.fetch_industry_range(universe, dates=dates)
-    total_data = pd.merge(total_data, industry_category, on=['trade_date', 'code']).dropna()
-    total_data.dropna(inplace=True)
-    return total_data
-
-def fetch_factor_sets(**kwargs):
-    db_info = kwargs["db_info"]
-    factor_names = kwargs["factor_names"]
-    start_date = kwargs['start_date']
-    end_date = kwargs['end_date']
-    universe_name_list = kwargs['universe_name']
-    benchmark_code = kwargs['benchmark_code']
-    freq = kwargs['freq']
-  
-    engine = SqlEngine(db_info) # alpha-mind engine
-    engine191 = create_engine(db_info, poolclass=NullPool)
-    factors = fetch_factor(engine191, factor_names, start_date, end_date)
-    total_data = factor_combination(engine, factors, universe_name_list, start_date, end_date, freq)
-    return total_data
-
-
-
-#session = str('15609986886946081')
-session = str(int(time.time() * 1000000 + datetime.datetime.now().microsecond))
-alpha_list = []
-for i in range(31,32):
-    alpha_name = 'alpha_' + str(i)
-    alpha_list.append(alpha_name)
-
-
-db_info = 'postgresql+psycopg2://alpha:alpha@180.166.26.82:8889/alpha'
-
-total_data = fetch_factor_sets(db_info=db_info,
-                               factor_names=alpha_list, risk_styles=["SIZE"],
-                               start_date='2010-01-01', end_date='2018-12-31',
-                               universe_name=['zz500','hs300','ashare'],
-                               benchmark_code=905,
-                               freq='3b')
-try:
-    diff_sets = set(total_data.columns) - set(alpha_list)
-except:
-    import pdb
-    pdb.set_trace()
-
-
-grouped_list = []
-for alpha_name in alpha_list:
-    print(alpha_name, session)
-    #pdb.set_trace()
-    #print(cache_data.get_cache(session, alpha_name))
-    factors_list = list(diff_sets)
-    factors_list.append(alpha_name)
-    factors_sets = total_data[factors_list]
-
-    cache_data.set_cache(session, alpha_name, factors_sets.to_json(orient='records'))
-
-    analysis.factor_analysis(factor_name=alpha_name,risk_styles=['SIZE'],
-                             benchmark_code=905,
-                             session=session)
-

factor/factor_earning.py

@@ -33,6 +33,10 @@ from ultron.cluster.invoke.cache_data import cache_data
 
 
 class FactorEarning(FactorBase):
+    """
+    收益质量
+        --盈利相关
+    """
     def __init__(self, name):
         super(FactorEarning, self).__init__(name)