add ensemble

finrl/applications/stock_trading/ensemble_stock_trading.py

@@ -0,0 +1,164 @@
+def main():
+    import warnings
+    warnings.filterwarnings("ignore")
+    import pandas as pd
+    import numpy as np
+    import matplotlib
+    import matplotlib.pyplot as plt
+    # matplotlib.use('Agg')
+    import datetime
+
+
+    from finrl.config_tickers import DOW_30_TICKER
+    from finrl.meta.preprocessor.yahoodownloader import YahooDownloader
+    from finrl.meta.preprocessor.preprocessors import FeatureEngineer, data_split
+    from finrl.meta.env_stock_trading.env_stocktrading import StockTradingEnv
+    from finrl.agents.stablebaselines3.models import DRLAgent, DRLEnsembleAgent
+    from finrl.plot import backtest_stats, backtest_plot, get_daily_return, get_baseline
+
+    from pprint import pprint
+
+    import sys
+    sys.path.append("../FinRL-Library")
+
+    import itertools
+
+    import os
+    from finrl.main import check_and_make_directories
+    from finrl.config import (
+        DATA_SAVE_DIR,
+        TRAINED_MODEL_DIR,
+        TENSORBOARD_LOG_DIR,
+        RESULTS_DIR,
+        INDICATORS,
+        TRAIN_START_DATE,
+        TRAIN_END_DATE,
+        TEST_START_DATE,
+        TEST_END_DATE,
+        TRADE_START_DATE,
+        TRADE_END_DATE,
+    )
+
+    check_and_make_directories([DATA_SAVE_DIR, TRAINED_MODEL_DIR, TENSORBOARD_LOG_DIR, RESULTS_DIR])
+    print(DOW_30_TICKER)
+    TRAIN_START_DATE = '2009-04-01'
+    TRAIN_END_DATE = '2021-01-01'
+    TEST_START_DATE = '2021-01-01'
+    TEST_END_DATE = '2022-06-01'
+
+    df = YahooDownloader(start_date=TRAIN_START_DATE,
+                         end_date=TEST_END_DATE,
+                         ticker_list=DOW_30_TICKER).fetch_data()
+
+    df.sort_values(['date', 'tic']).head()
+
+    fe = FeatureEngineer(use_technical_indicator=True,
+                         tech_indicator_list=INDICATORS,
+                         use_turbulence=True,
+                         user_defined_feature=False)
+
+    processed = fe.preprocess_data(df)
+    processed = processed.copy()
+    processed = processed.fillna(0)
+    processed = processed.replace(np.inf, 0)
+
+    stock_dimension = len(processed.tic.unique())
+    state_space = 1 + 2 * stock_dimension + len(INDICATORS) * stock_dimension
+    print(f"Stock Dimension: {stock_dimension}, State Space: {state_space}")
+
+    env_kwargs = {
+        "hmax": 100,
+        "initial_amount": 1000000,
+        "buy_cost_pct": 0.001,
+        "sell_cost_pct": 0.001,
+        "state_space": state_space,
+        "stock_dim": stock_dimension,
+        "tech_indicator_list": INDICATORS,
+        "action_space": stock_dimension,
+        "reward_scaling": 1e-4,
+        "print_verbosity": 5
+
+    }
+
+    rebalance_window = 63  # rebalance_window is the number of days to retrain the model
+    validation_window = 63  # validation_window is the number of days to do validation and trading (e.g. if validation_window=63, then both validation and trading period will be 63 days)
+
+    ensemble_agent = DRLEnsembleAgent(df=processed,
+                                      train_period=(TRAIN_START_DATE, TRAIN_END_DATE),
+                                      val_test_period=(TEST_START_DATE, TEST_END_DATE),
+                                      rebalance_window=rebalance_window,
+                                      validation_window=validation_window,
+                                      **env_kwargs)
+
+    A2C_model_kwargs = {
+        'n_steps': 5,
+        'ent_coef': 0.005,
+        'learning_rate': 0.0007
+    }
+
+    PPO_model_kwargs = {
+        "ent_coef": 0.01,
+        "n_steps": 2048,
+        "learning_rate": 0.00025,
+        "batch_size": 128
+    }
+
+    DDPG_model_kwargs = {
+        # "action_noise":"ornstein_uhlenbeck",
+        "buffer_size": 10_000,
+        "learning_rate": 0.0005,
+        "batch_size": 64
+    }
+
+    timesteps_dict = {'a2c': 10_000,
+                      'ppo': 10_000,
+                      'ddpg': 10_000
+                      }
+    df_summary = ensemble_agent.run_ensemble_strategy(A2C_model_kwargs,
+                                                      PPO_model_kwargs,
+                                                      DDPG_model_kwargs,
+                                                      timesteps_dict)
+
+    unique_trade_date = processed[(processed.date > TEST_START_DATE) & (processed.date <= TEST_END_DATE)].date.unique()
+
+    df_trade_date = pd.DataFrame({'datadate': unique_trade_date})
+
+    df_account_value = pd.DataFrame()
+    for i in range(rebalance_window + validation_window, len(unique_trade_date) + 1, rebalance_window):
+        temp = pd.read_csv('results/account_value_trade_{}_{}.csv'.format('ensemble', i))
+        df_account_value = df_account_value.append(temp, ignore_index=True)
+    sharpe = (252 ** 0.5) * df_account_value.account_value.pct_change(
+        1).mean() / df_account_value.account_value.pct_change(1).std()
+    print('Sharpe Ratio: ', sharpe)
+    df_account_value = df_account_value.join(df_trade_date[validation_window:].reset_index(drop=True))
+
+    df_account_value.account_value.plot()
+
+    print("==============Get Backtest Results===========")
+    now = datetime.datetime.now().strftime('%Y%m%d-%Hh%M')
+
+    perf_stats_all = backtest_stats(account_value=df_account_value)
+    perf_stats_all = pd.DataFrame(perf_stats_all)
+
+    # baseline stats
+    print("==============Get Baseline Stats===========")
+    baseline_df = get_baseline(
+        ticker="^DJI",
+        start=df_account_value.loc[0, 'date'],
+        end=df_account_value.loc[len(df_account_value) - 1, 'date'])
+
+    stats = backtest_stats(baseline_df, value_col_name='close')
+
+    print("==============Compare to DJIA===========")
+
+    # S&P 500: ^GSPC
+    # Dow Jones Index: ^DJI
+    # NASDAQ 100: ^NDX
+    backtest_plot(df_account_value,
+                  baseline_ticker='^DJI',
+                  baseline_start=df_account_value.loc[0, 'date'],
+                  baseline_end=df_account_value.loc[len(df_account_value) - 1, 'date'])
+
+
+
+