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fundamental_portfolio_drl.py

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+import warnings
+warnings.filterwarnings("ignore")
+
+import pandas as pd
+import numpy as np
+from pypfopt.efficient_frontier import EfficientFrontier
+from pypfopt import risk_models
+from pypfopt.risk_models import CovarianceShrinkage
+from pypfopt import expected_returns
+from datetime import datetime
+from pandas.tseries.offsets import BDay
+
+from finrl.agents.stablebaselines3.models import DRLAgent
+from finrl.meta.env_portfolio_allocation.env_portfolio import StockPortfolioEnv
+from finrl.meta.preprocessor.preprocessors import FeatureEngineer
+from finrl.meta.preprocessor.preprocessors import data_split
+from finrl import config
+import pickle
+
+from rl_model import run_models
+
+df_price = pd.read_csv("/home/wenbiaolin/20221117/sp500_price_19960101_20221021.csv")
+
+df_price['adjcp'] = df_price['prccd'] / df_price['ajexdi']
+
+df_price['date'] = df_price['datadate']
+df_price['open'] = df_price['prcod']
+df_price['close'] = df_price['prccd']
+df_price['high'] = df_price['prchd']
+df_price['low'] = df_price['prcld']
+df_price['volume'] =df_price['cshtrd']
+
+df = df_price[['date', 'open', 'close', 'high', 'low','adjcp','volume', 'gvkey']]
+
+df['tic'] = df_price['gvkey']
+
+df['date'] = pd.to_datetime(df['date'], format='%Y%m%d')
+df['day'] = [x.weekday() for x in df['date']]
+df.drop_duplicates(['gvkey', 'date'], inplace=True)
+selected_stock = pd.read_csv("stock_selected_rf.csv")
+
+trade_date=selected_stock.trade_date.unique()
+
+with open('all_return_table.pickle', 'rb') as handle:
+    all_return_table = pickle.load(handle)
+
+with open('all_stocks_info.pickle', 'rb') as handle:
+    all_stocks_info = pickle.load(handle)
+
+
+df_dict = {'trade_date':[], 'gvkey':[], 'weights':[]}
+testing_window = pd.Timedelta(np.timedelta64(1,'Y'))
+max_rolling_window = pd.Timedelta(np.timedelta64(10, 'Y'))
+
+
+for idx in range(1, len(trade_date)):
+    p1_alldata=all_stocks_info[trade_date[idx-1]]
+    p1_alldata=p1_alldata.sort_values('gvkey')
+    p1_alldata = p1_alldata.reset_index()
+    del p1_alldata['index']
+    p1_stock = p1_alldata.gvkey
+
+    earliest_date = pd.to_datetime(trade_date[idx-1]) - max_rolling_window
+
+    df_ = df[df['tic'].isin(p1_stock) & (df['date'] >= earliest_date) & (df['date'] < trade_date[idx])]
+    print(df_)
+    fe = FeatureEngineer(
+                    use_technical_indicator=True,
+                    use_turbulence=False,
+                    user_defined_feature = False)
+
+    df_ = fe.preprocess_data(df_)
+
+    df_=df_.sort_values(['date','tic'],ignore_index=True)
+    df_.index = df_.date.factorize()[0]
+
+    cov_list = []
+    return_list = []
+
+# look back is one year
+    lookback=252
+    for i in range(lookback,len(df_.index.unique())):
+        data_lookback = df_.loc[i-lookback:i,:]
+        price_lookback=data_lookback.pivot_table(index = 'date',columns = 'tic', values = 'close')
+        return_lookback = price_lookback.pct_change().dropna()
+        return_list.append(return_lookback)
+
+        covs = return_lookback.cov().values
+        cov_list.append(covs)
+
+
+    df_cov = pd.DataFrame({'date':df_.date.unique()[lookback:],'cov_list':cov_list,'return_list':return_list})
+    df_ = df_.merge(df_cov, on='date')
+    df_ = df_.sort_values(['date','tic']).reset_index(drop=True)
+
+    stock_dimension = len(df_.tic.unique())
+    state_space = stock_dimension
+    env_kwargs = {
+    "hmax": 100, 
+    "initial_amount": 1000000, 
+    "transaction_cost_pct": 0.001, 
+    "state_space": state_space, 
+    "stock_dim": stock_dimension, 
+    "tech_indicator_list": config.INDICATORS, 
+    "action_space": stock_dimension, 
+    "reward_scaling": 1e-4
+
+    }
+
+
+    a2c_model,ppo_model,ddpg_model,td3_model,sac_model,best_model = run_models(df_, "date", pd.to_datetime(trade_date[idx-1]), env_kwargs,testing_window, max_rolling_window)
+
+    trade = data_split(df_, pd.to_datetime(trade_date[idx-1]), pd.to_datetime(trade_date[idx]))
+    e_trade_gym = StockPortfolioEnv(df = trade, **env_kwargs)
+    df_daily_return, df_actions = DRLAgent.DRL_prediction(
+    model=a2c_model, environment=e_trade_gym
+    )
+
+
+    for i in range(len(df_actions)):
+        for j in df_actions.columns:
+            df_dict['trade_date'].append(df_actions.index[i])
+            df_dict['gvkey'].append(j)
+            df_dict['weights'].append(df_actions.loc[df_actions.index[i], j])
+
+
+df_rl = pd.DataFrame(df_dict)
+df_rl.to_csv("drl_weight.csv")