From 2c59c6b0f3e685cd5a4faa8b286ee77ceb1f60ab Mon Sep 17 00:00:00 2001 From: jookie Date: Sat, 21 Sep 2024 12:40:13 -0400 Subject: [PATCH] vercel-fix --- actions_a2c.csv | 80 +++ actions_ddpg.csv | 80 +++ actions_ppo.csv | 80 +++ actions_sac.csv | 80 +++ actions_td3.csv | 80 +++ docs/MD/t.md | 11 + examples/FINRL/1.py | 0 examples/FINRL/3.py | 463 ++++++++++++++++++ .../FINRL/stock_trading_rolling_window.py | 463 ++++++++++++++++++ examples/PY/.gitignore | 1 - examples/PY/{ => resuts}/actor.pth | Bin result.csv | 84 ++++ ...events.1726691915.Dovs-Mac-mini.lan.8454.0 | Bin 21096 -> 0 bytes ...vents.1726848159.Dovs-Mac-mini.lan.11463.0 | Bin 0 -> 44132 bytes ...vents.1726849559.Dovs-Mac-mini.lan.11463.5 | Bin 0 -> 44132 bytes ...ents.1726850992.Dovs-Mac-mini.lan.11463.10 | Bin 0 -> 44132 bytes ...ents.1726852420.Dovs-Mac-mini.lan.11463.15 | Bin 0 -> 44132 bytes results/a2c/progress.csv | 150 ++++-- ...vents.1726848357.Dovs-Mac-mini.lan.11463.1 | Bin 0 -> 627 bytes ...vents.1726849760.Dovs-Mac-mini.lan.11463.6 | Bin 0 -> 627 bytes ...ents.1726851190.Dovs-Mac-mini.lan.11463.11 | Bin 0 -> 627 bytes ...ents.1726852619.Dovs-Mac-mini.lan.11463.16 | Bin 0 -> 899 bytes results/ddpg/progress.csv | 4 + ...vents.1726848633.Dovs-Mac-mini.lan.11463.2 | Bin 0 -> 16242 bytes ...vents.1726850059.Dovs-Mac-mini.lan.11463.7 | Bin 0 -> 16242 bytes ...ents.1726851471.Dovs-Mac-mini.lan.11463.12 | Bin 0 -> 16242 bytes ...ents.1726852901.Dovs-Mac-mini.lan.11463.17 | Bin 0 -> 16242 bytes results/ppo/progress.csv | 26 + ...vents.1726848820.Dovs-Mac-mini.lan.11463.3 | Bin 0 -> 1236 bytes ...vents.1726850250.Dovs-Mac-mini.lan.11463.8 | Bin 0 -> 1236 bytes ...ents.1726851663.Dovs-Mac-mini.lan.11463.13 | Bin 0 -> 1236 bytes ...ents.1726853089.Dovs-Mac-mini.lan.11463.18 | Bin 0 -> 1236 bytes results/sac/progress.csv | 4 + ...vents.1726849198.Dovs-Mac-mini.lan.11463.4 | Bin 0 -> 899 bytes ...vents.1726850635.Dovs-Mac-mini.lan.11463.9 | Bin 0 -> 899 bytes ...ents.1726852063.Dovs-Mac-mini.lan.11463.14 | Bin 0 -> 899 bytes ...ents.1726853477.Dovs-Mac-mini.lan.11463.19 | Bin 0 -> 899 bytes results/td3/progress.csv | 4 + stock_trading_rolling_window.1.png | Bin 0 -> 131859 bytes stock_trading_rolling_window.png | Bin 0 -> 138604 bytes 40 files changed, 1560 insertions(+), 50 deletions(-) create mode 100644 actions_a2c.csv create mode 100644 actions_ddpg.csv create mode 100644 actions_ppo.csv create mode 100644 actions_sac.csv create mode 100644 actions_td3.csv create mode 100644 docs/MD/t.md create mode 100644 examples/FINRL/1.py create mode 100644 examples/FINRL/3.py create mode 100644 examples/FINRL/FINRL/stock_trading_rolling_window.py delete mode 100644 examples/PY/.gitignore rename examples/PY/{ => resuts}/actor.pth (100%) create mode 100644 result.csv delete mode 100644 results/a2c/events.out.tfevents.1726691915.Dovs-Mac-mini.lan.8454.0 create mode 100644 results/a2c/events.out.tfevents.1726848159.Dovs-Mac-mini.lan.11463.0 create mode 100644 results/a2c/events.out.tfevents.1726849559.Dovs-Mac-mini.lan.11463.5 create mode 100644 results/a2c/events.out.tfevents.1726850992.Dovs-Mac-mini.lan.11463.10 create mode 100644 results/a2c/events.out.tfevents.1726852420.Dovs-Mac-mini.lan.11463.15 create mode 100644 results/ddpg/events.out.tfevents.1726848357.Dovs-Mac-mini.lan.11463.1 create mode 100644 results/ddpg/events.out.tfevents.1726849760.Dovs-Mac-mini.lan.11463.6 create mode 100644 results/ddpg/events.out.tfevents.1726851190.Dovs-Mac-mini.lan.11463.11 create mode 100644 results/ddpg/events.out.tfevents.1726852619.Dovs-Mac-mini.lan.11463.16 create mode 100644 results/ddpg/progress.csv create mode 100644 results/ppo/events.out.tfevents.1726848633.Dovs-Mac-mini.lan.11463.2 create mode 100644 results/ppo/events.out.tfevents.1726850059.Dovs-Mac-mini.lan.11463.7 create mode 100644 results/ppo/events.out.tfevents.1726851471.Dovs-Mac-mini.lan.11463.12 create mode 100644 results/ppo/events.out.tfevents.1726852901.Dovs-Mac-mini.lan.11463.17 create mode 100644 results/ppo/progress.csv create mode 100644 results/sac/events.out.tfevents.1726848820.Dovs-Mac-mini.lan.11463.3 create mode 100644 results/sac/events.out.tfevents.1726850250.Dovs-Mac-mini.lan.11463.8 create mode 100644 results/sac/events.out.tfevents.1726851663.Dovs-Mac-mini.lan.11463.13 create mode 100644 results/sac/events.out.tfevents.1726853089.Dovs-Mac-mini.lan.11463.18 create mode 100644 results/sac/progress.csv create mode 100644 results/td3/events.out.tfevents.1726849198.Dovs-Mac-mini.lan.11463.4 create mode 100644 results/td3/events.out.tfevents.1726850635.Dovs-Mac-mini.lan.11463.9 create mode 100644 results/td3/events.out.tfevents.1726852063.Dovs-Mac-mini.lan.11463.14 create mode 100644 results/td3/events.out.tfevents.1726853477.Dovs-Mac-mini.lan.11463.19 create mode 100644 results/td3/progress.csv create mode 100644 stock_trading_rolling_window.1.png create mode 100644 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file diff --git a/examples/FINRL/1.py b/examples/FINRL/1.py new file mode 100644 index 0000000..e69de29 diff --git a/examples/FINRL/3.py b/examples/FINRL/3.py new file mode 100644 index 0000000..2db4afc --- /dev/null +++ b/examples/FINRL/3.py @@ -0,0 +1,463 @@ +from __future__ import annotations + +import copy +import datetime +import itertools +import os +import sys + +import matplotlib +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +from stable_baselines3.common.logger import configure + +from finrl import config +from finrl import config_tickers +from finrl.agents.stablebaselines3.models import DRLAgent +from finrl.config import DATA_SAVE_DIR +from finrl.config import INDICATORS +from finrl.config import RESULTS_DIR +from finrl.config import TENSORBOARD_LOG_DIR +from finrl.config import TEST_END_DATE +from finrl.config import TEST_START_DATE +from finrl.config import TRAINED_MODEL_DIR +from finrl.config_tickers import DOW_30_TICKER +from finrl.main import check_and_make_directories +from finrl.meta.data_processor import DataProcessor +from finrl.meta.data_processors.func import calc_train_trade_data +from finrl.meta.data_processors.func import calc_train_trade_starts_ends_if_rolling +from finrl.meta.data_processors.func import date2str +from finrl.meta.data_processors.func import str2date +from finrl.meta.env_stock_trading.env_stocktrading import StockTradingEnv +from finrl.meta.preprocessor.preprocessors import data_split +from finrl.meta.preprocessor.preprocessors import FeatureEngineer +from finrl.meta.preprocessor.yahoodownloader import YahooDownloader +from finrl.plot import backtest_plot +from finrl.plot import backtest_stats +from finrl.plot import get_baseline +from finrl.plot import get_daily_return +from finrl.plot import plot_return + +# matplotlib.use('Agg') + + +def stock_trading_rolling_window( + train_start_date: str, + train_end_date: str, + trade_start_date: str, + trade_end_date: str, + rolling_window_length: int, + if_store_actions: bool = True, + if_store_result: bool = True, + if_using_a2c: bool = True, + if_using_ddpg: bool = True, + if_using_ppo: bool = True, + if_using_sac: bool = True, + if_using_td3: bool = True, +): + # sys.path.append("../FinRL") + check_and_make_directories( + [DATA_SAVE_DIR, TRAINED_MODEL_DIR, TENSORBOARD_LOG_DIR, RESULTS_DIR] + ) + date_col = "date" + tic_col = "tic" + df = YahooDownloader( + start_date=train_start_date, end_date=trade_end_date, ticker_list=DOW_30_TICKER + ).fetch_data() + fe = FeatureEngineer( + use_technical_indicator=True, + tech_indicator_list=INDICATORS, + use_vix=True, + use_turbulence=True, + user_defined_feature=False, + ) + + processed = fe.preprocess_data(df) + list_ticker = processed[tic_col].unique().tolist() + list_date = list( + pd.date_range(processed[date_col].min(), processed[date_col].max()).astype(str) + ) + combination = list(itertools.product(list_date, list_ticker)) + + init_train_trade_data = pd.DataFrame( + combination, columns=[date_col, tic_col] + ).merge(processed, on=[date_col, tic_col], how="left") + init_train_trade_data = init_train_trade_data[ + init_train_trade_data[date_col].isin(processed[date_col]) + ] + init_train_trade_data = init_train_trade_data.sort_values([date_col, tic_col]) + + init_train_trade_data = init_train_trade_data.fillna(0) + + init_train_data = data_split( + init_train_trade_data, train_start_date, train_end_date + ) + init_trade_data = data_split( + init_train_trade_data, trade_start_date, trade_end_date + ) + + stock_dimension = len(init_train_data.tic.unique()) + state_space = 1 + 2 * stock_dimension + len(INDICATORS) * stock_dimension + print(f"Stock Dimension: {stock_dimension}, State Space: {state_space}") + buy_cost_list = sell_cost_list = [0.001] * stock_dimension + num_stock_shares = [0] * stock_dimension + + initial_amount = 1000000 + env_kwargs = { + "hmax": 100, + "initial_amount": initial_amount, + "num_stock_shares": num_stock_shares, + "buy_cost_pct": buy_cost_list, + "sell_cost_pct": sell_cost_list, + "state_space": state_space, + "stock_dim": stock_dimension, + "tech_indicator_list": INDICATORS, + "action_space": stock_dimension, + "reward_scaling": 1e-4, + } + + # split the init_train_data and init_trade_data to subsets + init_train_dates = init_train_data[date_col].unique() + init_trade_dates = init_trade_data[date_col].unique() + + ( + train_starts, + train_ends, + trade_starts, + trade_ends, + ) = calc_train_trade_starts_ends_if_rolling( + init_train_dates, init_trade_dates, rolling_window_length + ) + + result = pd.DataFrame() + actions_a2c = pd.DataFrame(columns=DOW_30_TICKER) + actions_ddpg = pd.DataFrame(columns=DOW_30_TICKER) + actions_ppo = pd.DataFrame(columns=DOW_30_TICKER) + actions_sac = pd.DataFrame(columns=DOW_30_TICKER) + actions_td3 = pd.DataFrame(columns=DOW_30_TICKER) + + for i in range(len(train_starts)): + print("i: ", i) + train_data, trade_data = calc_train_trade_data( + i, + train_starts, + train_ends, + trade_starts, + trade_ends, + init_train_data, + init_trade_data, + date_col, + ) + e_train_gym = StockTradingEnv(df=train_data, **env_kwargs) + env_train, _ = e_train_gym.get_sb_env() + + # train + + if if_using_a2c: + if len(result) >= 1: + env_kwargs["initial_amount"] = result["A2C"].iloc[-1] + e_train_gym = StockTradingEnv(df=train_data, **env_kwargs) + env_train, _ = e_train_gym.get_sb_env() + agent = DRLAgent(env=env_train) + model_a2c = agent.get_model("a2c") + # set up logger + tmp_path = RESULTS_DIR + "/a2c" + new_logger_a2c = configure(tmp_path, ["stdout", "csv", "tensorboard"]) + # Set new logger + model_a2c.set_logger(new_logger_a2c) + trained_a2c = agent.train_model( + model=model_a2c, tb_log_name="a2c", total_timesteps=50000 + ) + + if if_using_ddpg: + if len(result) >= 1: + env_kwargs["initial_amount"] = result["DDPG"].iloc[-1] + e_train_gym = StockTradingEnv(df=train_data, **env_kwargs) + env_train, _ = e_train_gym.get_sb_env() + agent = DRLAgent(env=env_train) + model_ddpg = agent.get_model("ddpg") + # set up logger + tmp_path = RESULTS_DIR + "/ddpg" + new_logger_ddpg = configure(tmp_path, ["stdout", "csv", "tensorboard"]) + # Set new logger + model_ddpg.set_logger(new_logger_ddpg) + trained_ddpg = agent.train_model( + model=model_ddpg, tb_log_name="ddpg", total_timesteps=40000 + ) + + if if_using_ppo: + if len(result) >= 1: + env_kwargs["initial_amount"] = result["PPO"].iloc[-1] + e_train_gym = StockTradingEnv(df=train_data, **env_kwargs) + env_train, _ = e_train_gym.get_sb_env() + agent = DRLAgent(env=env_train) + PPO_PARAMS = { + "n_steps": 2048, + "ent_coef": 0.005, + "learning_rate": 0.0001, + "batch_size": 64, + } + model_ppo = agent.get_model("ppo", model_kwargs=PPO_PARAMS) + # set up logger + tmp_path = RESULTS_DIR + "/ppo" + new_logger_ppo = configure(tmp_path, ["stdout", "csv", "tensorboard"]) + # Set new logger + model_ppo.set_logger(new_logger_ppo) + trained_ppo = agent.train_model( + model=model_ppo, tb_log_name="ppo", total_timesteps=50000 + ) + + if if_using_sac: + if len(result) >= 1: + env_kwargs["initial_amount"] = result["SAC"].iloc[-1] + e_train_gym = StockTradingEnv(df=train_data, **env_kwargs) + env_train, _ = e_train_gym.get_sb_env() + agent = DRLAgent(env=env_train) + SAC_PARAMS = { + "batch_size": 64, + "buffer_size": 100000, + "learning_rate": 0.00015, + "learning_starts": 100, + "ent_coef": "auto_0.1", + } + model_sac = agent.get_model("sac", model_kwargs=SAC_PARAMS) + # set up logger + tmp_path = RESULTS_DIR + "/sac" + new_logger_sac = configure(tmp_path, ["stdout", "csv", "tensorboard"]) + # Set new logger + model_sac.set_logger(new_logger_sac) + trained_sac = agent.train_model( + model=model_sac, tb_log_name="sac", total_timesteps=50000 + ) + + if if_using_td3: + if len(result) >= 1: + env_kwargs["initial_amount"] = result["TD3"].iloc[-1] + e_train_gym = StockTradingEnv(df=train_data, **env_kwargs) + env_train, _ = e_train_gym.get_sb_env() + agent = DRLAgent(env=env_train) + TD3_PARAMS = { + "batch_size": 64, + "buffer_size": 100000, + "learning_rate": 0.0008, + } + model_td3 = agent.get_model("td3", model_kwargs=TD3_PARAMS) + # set up logger + tmp_path = RESULTS_DIR + "/td3" + new_logger_td3 = configure(tmp_path, ["stdout", "csv", "tensorboard"]) + # Set new logger + model_td3.set_logger(new_logger_td3) + trained_td3 = agent.train_model( + model=model_td3, tb_log_name="td3", total_timesteps=50000 + ) + + # trade + # this e_trade_gym is initialized, then it will be used if i == 0 + e_trade_gym = StockTradingEnv( + df=trade_data, + turbulence_threshold=70, + risk_indicator_col="vix", + **env_kwargs, + ) + + if if_using_a2c: + if len(result) >= 1: + env_kwargs["initial_amount"] = result["A2C"].iloc[-1] + e_trade_gym = StockTradingEnv( + df=trade_data, + turbulence_threshold=70, + risk_indicator_col="vix", + **env_kwargs, + ) + result_a2c, actions_i_a2c = DRLAgent.DRL_prediction( + model=trained_a2c, environment=e_trade_gym + ) + + if if_using_ddpg: + if len(result) >= 1: + env_kwargs["initial_amount"] = result["DDPG"].iloc[-1] + e_trade_gym = StockTradingEnv( + df=trade_data, + turbulence_threshold=70, + risk_indicator_col="vix", + **env_kwargs, + ) + result_ddpg, actions_i_ddpg = DRLAgent.DRL_prediction( + model=trained_ddpg, environment=e_trade_gym + ) + + if if_using_ppo: + if len(result) >= 1: + env_kwargs["initial_amount"] = result["PPO"].iloc[-1] + e_trade_gym = StockTradingEnv( + df=trade_data, + turbulence_threshold=70, + risk_indicator_col="vix", + **env_kwargs, + ) + result_ppo, actions_i_ppo = DRLAgent.DRL_prediction( + model=trained_ppo, environment=e_trade_gym + ) + + if if_using_sac: + if len(result) >= 1: + env_kwargs["initial_amount"] = result["SAC"].iloc[-1] + e_trade_gym = StockTradingEnv( + df=trade_data, + turbulence_threshold=70, + risk_indicator_col="vix", + **env_kwargs, + ) + result_sac, actions_i_sac = DRLAgent.DRL_prediction( + model=trained_sac, environment=e_trade_gym + ) + + if if_using_td3: + if len(result) >= 1: + env_kwargs["initial_amount"] = result["TD3"].iloc[-1] + e_trade_gym = StockTradingEnv( + df=trade_data, + turbulence_threshold=70, + risk_indicator_col="vix", + **env_kwargs, + ) + result_td3, actions_i_td3 = DRLAgent.DRL_prediction( + model=trained_td3, environment=e_trade_gym + ) + + # in python version, we should check isinstance, but in notebook version, it is not necessary + if if_using_a2c and isinstance(result_a2c, tuple): + actions_i_a2c = result_a2c[1] + result_a2c = result_a2c[0] + if if_using_ddpg and isinstance(result_ddpg, tuple): + actions_i_ddpg = result_ddpg[1] + result_ddpg = result_ddpg[0] + if if_using_ppo and isinstance(result_ppo, tuple): + actions_i_ppo = result_ppo[1] + result_ppo = result_ppo[0] + if if_using_sac and isinstance(result_sac, tuple): + actions_i_sac = result_sac[1] + result_sac = result_sac[0] + if if_using_td3 and isinstance(result_td3, tuple): + actions_i_td3 = result_td3[1] + result_td3 = result_td3[0] + + # merge actions + actions_a2c = pd.concat([actions_a2c, actions_i_a2c]) if if_using_a2c else None + actions_ddpg = ( + pd.concat([actions_ddpg, actions_i_ddpg]) if if_using_ddpg else None + ) + actions_ppo = pd.concat([actions_ppo, actions_i_ppo]) if if_using_ppo else None + actions_sac = pd.concat([actions_sac, actions_i_sac]) if if_using_sac else None + actions_td3 = pd.concat([actions_td3, actions_i_td3]) if if_using_td3 else None + + # dji_i + trade_start = trade_starts[i] + trade_end = trade_ends[i] + dji_i_ = get_baseline(ticker="^DJI", start=trade_start, end=trade_end) + dji_i = pd.DataFrame() + dji_i[date_col] = dji_i_[date_col] + dji_i["DJI"] = dji_i_["close"] + # dji_i.rename(columns={'account_value': 'DJI'}, inplace=True) + + # select the rows between trade_start and trade_end (not included), since some values may not in this region + dji_i = dji_i.loc[ + (dji_i[date_col] >= trade_start) & (dji_i[date_col] < trade_end) + ] + + # init result_i by dji_i + result_i = dji_i + + # rename column name of result_a2c, result_ddpg, etc., and then put them to result_i + if if_using_a2c: + result_a2c.rename(columns={"account_value": "A2C"}, inplace=True) + result_i = pd.merge(result_i, result_a2c, how="left") + if if_using_ddpg: + result_ddpg.rename(columns={"account_value": "DDPG"}, inplace=True) + result_i = pd.merge(result_i, result_ddpg, how="left") + if if_using_ppo: + result_ppo.rename(columns={"account_value": "PPO"}, inplace=True) + result_i = pd.merge(result_i, result_ppo, how="left") + if if_using_sac: + result_sac.rename(columns={"account_value": "SAC"}, inplace=True) + result_i = pd.merge(result_i, result_sac, how="left") + if if_using_td3: + result_td3.rename(columns={"account_value": "TD3"}, inplace=True) + result_i = pd.merge(result_i, result_td3, how="left") + + # remove the rows with nan + result_i = result_i.dropna(axis=0, how="any") + + # merge result_i to result + result = pd.concat([result, result_i], axis=0) + + # store actions + if if_store_actions: + actions_a2c.to_csv("actions_a2c.csv") if if_using_a2c else None + actions_ddpg.to_csv("actions_ddpg.csv") if if_using_ddpg else None + actions_ppo.to_csv("actions_ppo.csv") if if_using_ppo else None + actions_sac.to_csv("actions_sac.csv") if if_using_sac else None + actions_td3.to_csv("actions_td3.csv") if if_using_td3 else None + + # calc the column name of strategies, including DJI + col_strategies = [] + for col in result.columns: + if col != date_col and col != "" and "Unnamed" not in col: + col_strategies.append(col) + + # make sure that the first row of DJI is initial_amount + col = "DJI" + result[col] = result[col] / result[col].iloc[0] * initial_amount + result = result.reset_index(drop=True) + + # stats + for col in col_strategies: + stats = backtest_stats(result, value_col_name=col) + print("\nstats of " + col + ": \n", stats) + + # print and save result + print("result: ", result) + if if_store_result: + result.to_csv("result.csv") + + # plot fig + plot_return( + result=result, + column_as_x=date_col, + if_need_calc_return=True, + savefig_filename="stock_trading_rolling_window.png", + xlabel="Date", + ylabel="Return", + if_transfer_date=True, + num_days_xticks=20, + ) + + +if __name__ == "__main__": + train_start_date = "2009-01-01" + train_end_date = "2022-07-01" + trade_start_date = "2022-07-01" + trade_end_date = "2022-11-01" + rolling_window_length = 22 # num of trading days in a rolling window + if_store_actions = True + if_store_result = True + if_using_a2c = True + if_using_ddpg = True + if_using_ppo = True + if_using_sac = True + if_using_td3 = True + stock_trading_rolling_window( + train_start_date=train_start_date, + train_end_date=train_end_date, + trade_start_date=trade_start_date, + trade_end_date=trade_end_date, + rolling_window_length=rolling_window_length, + if_store_actions=if_store_actions, + if_store_result=if_store_result, + if_using_a2c=if_using_a2c, + if_using_ddpg=if_using_ddpg, + if_using_ppo=if_using_ppo, + if_using_sac=if_using_sac, + if_using_td3=if_using_td3, + ) diff --git a/examples/FINRL/FINRL/stock_trading_rolling_window.py b/examples/FINRL/FINRL/stock_trading_rolling_window.py new file mode 100644 index 0000000..2db4afc --- /dev/null +++ b/examples/FINRL/FINRL/stock_trading_rolling_window.py @@ -0,0 +1,463 @@ +from __future__ import annotations + +import copy +import datetime +import itertools +import os +import sys + +import matplotlib +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +from stable_baselines3.common.logger import configure + +from finrl import config +from finrl import config_tickers +from finrl.agents.stablebaselines3.models import DRLAgent +from finrl.config import DATA_SAVE_DIR +from finrl.config import INDICATORS +from finrl.config import RESULTS_DIR +from finrl.config import TENSORBOARD_LOG_DIR +from finrl.config import TEST_END_DATE +from finrl.config import TEST_START_DATE +from finrl.config import TRAINED_MODEL_DIR +from finrl.config_tickers import DOW_30_TICKER +from finrl.main import check_and_make_directories +from finrl.meta.data_processor import DataProcessor +from finrl.meta.data_processors.func import calc_train_trade_data +from finrl.meta.data_processors.func import calc_train_trade_starts_ends_if_rolling +from finrl.meta.data_processors.func import date2str +from finrl.meta.data_processors.func import str2date +from finrl.meta.env_stock_trading.env_stocktrading import StockTradingEnv +from finrl.meta.preprocessor.preprocessors import data_split +from finrl.meta.preprocessor.preprocessors import FeatureEngineer +from finrl.meta.preprocessor.yahoodownloader import YahooDownloader +from finrl.plot import backtest_plot +from finrl.plot import backtest_stats +from finrl.plot import get_baseline +from finrl.plot import get_daily_return +from finrl.plot import plot_return + +# matplotlib.use('Agg') + + +def stock_trading_rolling_window( + train_start_date: str, + train_end_date: str, + trade_start_date: str, + trade_end_date: str, + rolling_window_length: int, + if_store_actions: bool = True, + if_store_result: bool = True, + if_using_a2c: bool = True, + if_using_ddpg: bool = True, + if_using_ppo: bool = True, + if_using_sac: bool = True, + if_using_td3: bool = True, +): + # sys.path.append("../FinRL") + check_and_make_directories( + [DATA_SAVE_DIR, TRAINED_MODEL_DIR, TENSORBOARD_LOG_DIR, RESULTS_DIR] + ) + date_col = "date" + tic_col = "tic" + df = YahooDownloader( + start_date=train_start_date, end_date=trade_end_date, ticker_list=DOW_30_TICKER + ).fetch_data() + fe = FeatureEngineer( + use_technical_indicator=True, + tech_indicator_list=INDICATORS, + use_vix=True, + use_turbulence=True, + user_defined_feature=False, + ) + + processed = fe.preprocess_data(df) + list_ticker = processed[tic_col].unique().tolist() + list_date = list( + pd.date_range(processed[date_col].min(), processed[date_col].max()).astype(str) + ) + combination = list(itertools.product(list_date, list_ticker)) + + init_train_trade_data = pd.DataFrame( + combination, columns=[date_col, tic_col] + ).merge(processed, on=[date_col, tic_col], how="left") + init_train_trade_data = init_train_trade_data[ + init_train_trade_data[date_col].isin(processed[date_col]) + ] + init_train_trade_data = init_train_trade_data.sort_values([date_col, tic_col]) + + init_train_trade_data = init_train_trade_data.fillna(0) + + init_train_data = data_split( + init_train_trade_data, train_start_date, train_end_date + ) + init_trade_data = data_split( + init_train_trade_data, trade_start_date, trade_end_date + ) + + stock_dimension = len(init_train_data.tic.unique()) + state_space = 1 + 2 * stock_dimension + len(INDICATORS) * stock_dimension + print(f"Stock Dimension: {stock_dimension}, State Space: {state_space}") + buy_cost_list = sell_cost_list = [0.001] * stock_dimension + num_stock_shares = [0] * stock_dimension + + initial_amount = 1000000 + env_kwargs = { + "hmax": 100, + "initial_amount": initial_amount, + "num_stock_shares": num_stock_shares, + "buy_cost_pct": buy_cost_list, + "sell_cost_pct": sell_cost_list, + "state_space": state_space, + "stock_dim": stock_dimension, + "tech_indicator_list": INDICATORS, + "action_space": stock_dimension, + "reward_scaling": 1e-4, + } + + # split the init_train_data and init_trade_data to subsets + init_train_dates = init_train_data[date_col].unique() + init_trade_dates = init_trade_data[date_col].unique() + + ( + train_starts, + train_ends, + trade_starts, + trade_ends, + ) = calc_train_trade_starts_ends_if_rolling( + init_train_dates, init_trade_dates, rolling_window_length + ) + + result = pd.DataFrame() + actions_a2c = pd.DataFrame(columns=DOW_30_TICKER) + actions_ddpg = pd.DataFrame(columns=DOW_30_TICKER) + actions_ppo = pd.DataFrame(columns=DOW_30_TICKER) + actions_sac = pd.DataFrame(columns=DOW_30_TICKER) + actions_td3 = pd.DataFrame(columns=DOW_30_TICKER) + + for i in range(len(train_starts)): + print("i: ", i) + train_data, trade_data = calc_train_trade_data( + i, + train_starts, + train_ends, + trade_starts, + trade_ends, + init_train_data, + init_trade_data, + date_col, + ) + e_train_gym = StockTradingEnv(df=train_data, **env_kwargs) + env_train, _ = e_train_gym.get_sb_env() + + # train + + if if_using_a2c: + if len(result) >= 1: + env_kwargs["initial_amount"] = result["A2C"].iloc[-1] + e_train_gym = StockTradingEnv(df=train_data, **env_kwargs) + env_train, _ = e_train_gym.get_sb_env() + agent = DRLAgent(env=env_train) + model_a2c = agent.get_model("a2c") + # set up logger + tmp_path = RESULTS_DIR + "/a2c" + new_logger_a2c = configure(tmp_path, ["stdout", "csv", "tensorboard"]) + # Set new logger + model_a2c.set_logger(new_logger_a2c) + trained_a2c = agent.train_model( + model=model_a2c, tb_log_name="a2c", total_timesteps=50000 + ) + + if if_using_ddpg: + if len(result) >= 1: + env_kwargs["initial_amount"] = result["DDPG"].iloc[-1] + e_train_gym = StockTradingEnv(df=train_data, **env_kwargs) + env_train, _ = e_train_gym.get_sb_env() + agent = DRLAgent(env=env_train) + model_ddpg = agent.get_model("ddpg") + # set up logger + tmp_path = RESULTS_DIR + "/ddpg" + new_logger_ddpg = configure(tmp_path, ["stdout", "csv", "tensorboard"]) + # Set new logger + model_ddpg.set_logger(new_logger_ddpg) + trained_ddpg = agent.train_model( + model=model_ddpg, tb_log_name="ddpg", total_timesteps=40000 + ) + + if if_using_ppo: + if len(result) >= 1: + env_kwargs["initial_amount"] = result["PPO"].iloc[-1] + e_train_gym = StockTradingEnv(df=train_data, **env_kwargs) + env_train, _ = e_train_gym.get_sb_env() + agent = DRLAgent(env=env_train) + PPO_PARAMS = { + "n_steps": 2048, + "ent_coef": 0.005, + "learning_rate": 0.0001, + "batch_size": 64, + } + model_ppo = agent.get_model("ppo", model_kwargs=PPO_PARAMS) + # set up logger + tmp_path = RESULTS_DIR + "/ppo" + new_logger_ppo = configure(tmp_path, ["stdout", "csv", "tensorboard"]) + # Set new logger + model_ppo.set_logger(new_logger_ppo) + trained_ppo = agent.train_model( + model=model_ppo, tb_log_name="ppo", total_timesteps=50000 + ) + + if if_using_sac: + if len(result) >= 1: + env_kwargs["initial_amount"] = result["SAC"].iloc[-1] + e_train_gym = StockTradingEnv(df=train_data, **env_kwargs) + env_train, _ = e_train_gym.get_sb_env() + agent = DRLAgent(env=env_train) + SAC_PARAMS = { + "batch_size": 64, + "buffer_size": 100000, + "learning_rate": 0.00015, + "learning_starts": 100, + "ent_coef": "auto_0.1", + } + model_sac = agent.get_model("sac", model_kwargs=SAC_PARAMS) + # set up logger + tmp_path = RESULTS_DIR + "/sac" + new_logger_sac = configure(tmp_path, ["stdout", "csv", "tensorboard"]) + # Set new logger + model_sac.set_logger(new_logger_sac) + trained_sac = agent.train_model( + model=model_sac, tb_log_name="sac", total_timesteps=50000 + ) + + if if_using_td3: + if len(result) >= 1: + env_kwargs["initial_amount"] = result["TD3"].iloc[-1] + e_train_gym = StockTradingEnv(df=train_data, **env_kwargs) + env_train, _ = e_train_gym.get_sb_env() + agent = DRLAgent(env=env_train) + TD3_PARAMS = { + "batch_size": 64, + "buffer_size": 100000, + "learning_rate": 0.0008, + } + model_td3 = agent.get_model("td3", model_kwargs=TD3_PARAMS) + # set up logger + tmp_path = RESULTS_DIR + "/td3" + new_logger_td3 = configure(tmp_path, ["stdout", "csv", "tensorboard"]) + # Set new logger + model_td3.set_logger(new_logger_td3) + trained_td3 = agent.train_model( + model=model_td3, tb_log_name="td3", total_timesteps=50000 + ) + + # trade + # this e_trade_gym is initialized, then it will be used if i == 0 + e_trade_gym = StockTradingEnv( + df=trade_data, + turbulence_threshold=70, + risk_indicator_col="vix", + **env_kwargs, + ) + + if if_using_a2c: + if len(result) >= 1: + env_kwargs["initial_amount"] = result["A2C"].iloc[-1] + e_trade_gym = StockTradingEnv( + df=trade_data, + turbulence_threshold=70, + risk_indicator_col="vix", + **env_kwargs, + ) + result_a2c, actions_i_a2c = DRLAgent.DRL_prediction( + model=trained_a2c, environment=e_trade_gym + ) + + if if_using_ddpg: + if len(result) >= 1: + env_kwargs["initial_amount"] = result["DDPG"].iloc[-1] + e_trade_gym = StockTradingEnv( + df=trade_data, + turbulence_threshold=70, + risk_indicator_col="vix", + **env_kwargs, + ) + result_ddpg, actions_i_ddpg = DRLAgent.DRL_prediction( + model=trained_ddpg, environment=e_trade_gym + ) + + if if_using_ppo: + if len(result) >= 1: + env_kwargs["initial_amount"] = result["PPO"].iloc[-1] + e_trade_gym = StockTradingEnv( + df=trade_data, + turbulence_threshold=70, + risk_indicator_col="vix", + **env_kwargs, + ) + result_ppo, actions_i_ppo = DRLAgent.DRL_prediction( + model=trained_ppo, environment=e_trade_gym + ) + + if if_using_sac: + if len(result) >= 1: + env_kwargs["initial_amount"] = result["SAC"].iloc[-1] + e_trade_gym = StockTradingEnv( + df=trade_data, + turbulence_threshold=70, + risk_indicator_col="vix", + **env_kwargs, + ) + result_sac, actions_i_sac = DRLAgent.DRL_prediction( + model=trained_sac, environment=e_trade_gym + ) + + if if_using_td3: + if len(result) >= 1: + env_kwargs["initial_amount"] = result["TD3"].iloc[-1] + e_trade_gym = StockTradingEnv( + df=trade_data, + turbulence_threshold=70, + risk_indicator_col="vix", + **env_kwargs, + ) + result_td3, actions_i_td3 = DRLAgent.DRL_prediction( + model=trained_td3, environment=e_trade_gym + ) + + # in python version, we should check isinstance, but in notebook version, it is not necessary + if if_using_a2c and isinstance(result_a2c, tuple): + actions_i_a2c = result_a2c[1] + result_a2c = result_a2c[0] + if if_using_ddpg and isinstance(result_ddpg, tuple): + actions_i_ddpg = result_ddpg[1] + result_ddpg = result_ddpg[0] + if if_using_ppo and isinstance(result_ppo, tuple): + actions_i_ppo = result_ppo[1] + result_ppo = result_ppo[0] + if if_using_sac and isinstance(result_sac, tuple): + actions_i_sac = result_sac[1] + result_sac = result_sac[0] + if if_using_td3 and isinstance(result_td3, tuple): + actions_i_td3 = result_td3[1] + result_td3 = result_td3[0] + + # merge actions + actions_a2c = pd.concat([actions_a2c, actions_i_a2c]) if if_using_a2c else None + actions_ddpg = ( + pd.concat([actions_ddpg, actions_i_ddpg]) if if_using_ddpg else None + ) + actions_ppo = pd.concat([actions_ppo, actions_i_ppo]) if if_using_ppo else None + actions_sac = pd.concat([actions_sac, actions_i_sac]) if if_using_sac else None + actions_td3 = pd.concat([actions_td3, actions_i_td3]) if if_using_td3 else None + + # dji_i + trade_start = trade_starts[i] + trade_end = trade_ends[i] + dji_i_ = get_baseline(ticker="^DJI", start=trade_start, end=trade_end) + dji_i = pd.DataFrame() + dji_i[date_col] = dji_i_[date_col] + dji_i["DJI"] = dji_i_["close"] + # dji_i.rename(columns={'account_value': 'DJI'}, inplace=True) + + # select the rows between trade_start and trade_end (not included), since some values may not in this region + dji_i = dji_i.loc[ + (dji_i[date_col] >= trade_start) & (dji_i[date_col] < trade_end) + ] + + # init result_i by dji_i + result_i = dji_i + + # rename column name of result_a2c, result_ddpg, etc., and then put them to result_i + if if_using_a2c: + result_a2c.rename(columns={"account_value": "A2C"}, inplace=True) + result_i = pd.merge(result_i, result_a2c, how="left") + if if_using_ddpg: + result_ddpg.rename(columns={"account_value": "DDPG"}, inplace=True) + result_i = pd.merge(result_i, result_ddpg, how="left") + if if_using_ppo: + result_ppo.rename(columns={"account_value": "PPO"}, inplace=True) + result_i = pd.merge(result_i, result_ppo, how="left") + if if_using_sac: + result_sac.rename(columns={"account_value": "SAC"}, inplace=True) + result_i = pd.merge(result_i, result_sac, how="left") + if if_using_td3: + result_td3.rename(columns={"account_value": "TD3"}, inplace=True) + result_i = pd.merge(result_i, result_td3, how="left") + + # remove the rows with nan + result_i = result_i.dropna(axis=0, how="any") + + # merge result_i to result + result = pd.concat([result, result_i], axis=0) + + # store actions + if if_store_actions: + actions_a2c.to_csv("actions_a2c.csv") if if_using_a2c else None + actions_ddpg.to_csv("actions_ddpg.csv") if if_using_ddpg else None + actions_ppo.to_csv("actions_ppo.csv") if if_using_ppo else None + actions_sac.to_csv("actions_sac.csv") if if_using_sac else None + actions_td3.to_csv("actions_td3.csv") if if_using_td3 else None + + # calc the column name of strategies, including DJI + col_strategies = [] + for col in result.columns: + if col != date_col and col != "" and "Unnamed" not in col: + col_strategies.append(col) + + # make sure that the first row of DJI is initial_amount + col = "DJI" + result[col] = result[col] / result[col].iloc[0] * initial_amount + result = result.reset_index(drop=True) + + # stats + for col in col_strategies: + stats = backtest_stats(result, value_col_name=col) + print("\nstats of " + col + ": \n", stats) + + # print and save result + print("result: ", result) + if if_store_result: + result.to_csv("result.csv") + + # plot fig + plot_return( + result=result, + column_as_x=date_col, + if_need_calc_return=True, + savefig_filename="stock_trading_rolling_window.png", + xlabel="Date", + ylabel="Return", + if_transfer_date=True, + num_days_xticks=20, + ) + + +if __name__ == "__main__": + train_start_date = "2009-01-01" + train_end_date = "2022-07-01" + trade_start_date = "2022-07-01" + trade_end_date = "2022-11-01" + rolling_window_length = 22 # num of trading days in a rolling window + if_store_actions = True + if_store_result = True + if_using_a2c = True + if_using_ddpg = True + if_using_ppo = True + if_using_sac = True + if_using_td3 = True + stock_trading_rolling_window( + train_start_date=train_start_date, + train_end_date=train_end_date, + trade_start_date=trade_start_date, + trade_end_date=trade_end_date, + rolling_window_length=rolling_window_length, + if_store_actions=if_store_actions, + if_store_result=if_store_result, + if_using_a2c=if_using_a2c, + if_using_ddpg=if_using_ddpg, + if_using_ppo=if_using_ppo, + if_using_sac=if_using_sac, + if_using_td3=if_using_td3, + ) diff --git a/examples/PY/.gitignore b/examples/PY/.gitignore deleted file mode 100644 index e985853..0000000 --- a/examples/PY/.gitignore +++ /dev/null @@ -1 +0,0 @@ -.vercel diff --git a/examples/PY/actor.pth b/examples/PY/resuts/actor.pth similarity index 100% rename from examples/PY/actor.pth rename to examples/PY/resuts/actor.pth diff --git a/result.csv b/result.csv new file mode 100644 index 0000000..2745201 --- /dev/null +++ b/result.csv @@ -0,0 +1,84 @@ +,date,DJI,A2C,DDPG,PPO,SAC,TD3 +0,2022-07-01,1000000.0,1000000.0,1000000.0,1000000.0,1000000.0,1000000.0 +1,2022-07-05,995837.5929551168,998941.2123386305,997988.724350357,999999.9185631332,999592.2361364594,999007.4175331115 +2,2022-07-06,998084.0730477847,1000098.5496021953,1000434.7111297606,999978.9006493724,1000865.5817345846,1001369.2522304535 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