Revert "fix: Moved device parameter from DRLAgent initialization to g…

…et_model…" (#1280)

This reverts commit e11f729.

examples/FinRL_PaperTrading_Demo_refactored.py

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+# Disclaimer: Nothing herein is financial advice, and NOT a recommendation to trade real money. Many platforms exist for simulated trading (paper trading) which can be used for building and developing the methods discussed. Please use common sense and always first consult a professional before trading or investing.
+# install finrl library
+# %pip install --upgrade git+https://github.com/AI4Finance-Foundation/FinRL.git
+# Alpaca keys
+from __future__ import annotations
+
+import argparse
+
+parser = argparse.ArgumentParser()
+parser.add_argument("data_key", help="data source api key")
+parser.add_argument("data_secret", help="data source api secret")
+parser.add_argument("data_url", help="data source api base url")
+parser.add_argument("trading_key", help="trading api key")
+parser.add_argument("trading_secret", help="trading api secret")
+parser.add_argument("trading_url", help="trading api base url")
+args = parser.parse_args()
+DATA_API_KEY = args.data_key
+DATA_API_SECRET = args.data_secret
+DATA_API_BASE_URL = args.data_url
+TRADING_API_KEY = args.trading_key
+TRADING_API_SECRET = args.trading_secret
+TRADING_API_BASE_URL = args.trading_url
+
+print("DATA_API_KEY: ", DATA_API_KEY)
+print("DATA_API_SECRET: ", DATA_API_SECRET)
+print("DATA_API_BASE_URL: ", DATA_API_BASE_URL)
+print("TRADING_API_KEY: ", TRADING_API_KEY)
+print("TRADING_API_SECRET: ", TRADING_API_SECRET)
+print("TRADING_API_BASE_URL: ", TRADING_API_BASE_URL)
+
+from finrl.meta.env_stock_trading.env_stocktrading_np import StockTradingEnv
+from finrl.meta.paper_trading.alpaca import PaperTradingAlpaca
+from finrl.meta.paper_trading.common import train, test, alpaca_history, DIA_history
+from finrl.config import INDICATORS
+
+# Import Dow Jones 30 Symbols
+from finrl.config_tickers import DOW_30_TICKER
+
+ticker_list = DOW_30_TICKER
+env = StockTradingEnv
+# if you want to use larger datasets (change to longer period), and it raises error, please try to increase "target_step". It should be larger than the episode steps.
+ERL_PARAMS = {
+    "learning_rate": 3e-6,
+    "batch_size": 2048,
+    "gamma": 0.985,
+    "seed": 312,
+    "net_dimension": [128, 64],
+    "target_step": 5000,
+    "eval_gap": 30,
+    "eval_times": 1,
+}
+
+# Set up sliding window of 6 days training and 2 days testing
+import datetime
+from pandas.tseries.offsets import BDay  # BDay is business day, not birthday...
+
+today = datetime.datetime.today()
+
+TEST_END_DATE = (today - BDay(1)).to_pydatetime().date()
+TEST_START_DATE = (TEST_END_DATE - BDay(1)).to_pydatetime().date()
+TRAIN_END_DATE = (TEST_START_DATE - BDay(1)).to_pydatetime().date()
+TRAIN_START_DATE = (TRAIN_END_DATE - BDay(5)).to_pydatetime().date()
+TRAINFULL_START_DATE = TRAIN_START_DATE
+TRAINFULL_END_DATE = TEST_END_DATE
+
+TRAIN_START_DATE = str(TRAIN_START_DATE)
+TRAIN_END_DATE = str(TRAIN_END_DATE)
+TEST_START_DATE = str(TEST_START_DATE)
+TEST_END_DATE = str(TEST_END_DATE)
+TRAINFULL_START_DATE = str(TRAINFULL_START_DATE)
+TRAINFULL_END_DATE = str(TRAINFULL_END_DATE)
+
+print("TRAIN_START_DATE: ", TRAIN_START_DATE)
+print("TRAIN_END_DATE: ", TRAIN_END_DATE)
+print("TEST_START_DATE: ", TEST_START_DATE)
+print("TEST_END_DATE: ", TEST_END_DATE)
+print("TRAINFULL_START_DATE: ", TRAINFULL_START_DATE)
+print("TRAINFULL_END_DATE: ", TRAINFULL_END_DATE)
+
+train(
+    start_date=TRAIN_START_DATE,
+    end_date=TRAIN_END_DATE,
+    ticker_list=ticker_list,
+    data_source="alpaca",
+    time_interval="1Min",
+    technical_indicator_list=INDICATORS,
+    drl_lib="elegantrl",
+    env=env,
+    model_name="ppo",
+    if_vix=True,
+    API_KEY=DATA_API_KEY,
+    API_SECRET=DATA_API_SECRET,
+    API_BASE_URL=DATA_API_BASE_URL,
+    erl_params=ERL_PARAMS,
+    cwd="./papertrading_erl",  # current_working_dir
+    break_step=1e5,
+)
+
+account_value_erl = test(
+    start_date=TEST_START_DATE,
+    end_date=TEST_END_DATE,
+    ticker_list=ticker_list,
+    data_source="alpaca",
+    time_interval="1Min",
+    technical_indicator_list=INDICATORS,
+    drl_lib="elegantrl",
+    env=env,
+    model_name="ppo",
+    if_vix=True,
+    API_KEY=DATA_API_KEY,
+    API_SECRET=DATA_API_SECRET,
+    API_BASE_URL=DATA_API_BASE_URL,
+    cwd="./papertrading_erl",
+    net_dimension=ERL_PARAMS["net_dimension"],
+)
+
+train(
+    start_date=TRAINFULL_START_DATE,  # After tuning well, retrain on the training and testing sets
+    end_date=TRAINFULL_END_DATE,
+    ticker_list=ticker_list,
+    data_source="alpaca",
+    time_interval="1Min",
+    technical_indicator_list=INDICATORS,
+    drl_lib="elegantrl",
+    env=env,
+    model_name="ppo",
+    if_vix=True,
+    API_KEY=DATA_API_KEY,
+    API_SECRET=DATA_API_SECRET,
+    API_BASE_URL=DATA_API_BASE_URL,
+    erl_params=ERL_PARAMS,
+    cwd="./papertrading_erl_retrain",
+    break_step=2e5,
+)
+
+action_dim = len(DOW_30_TICKER)
+state_dim = (
+    1 + 2 + 3 * action_dim + len(INDICATORS) * action_dim
+)  # Calculate the DRL state dimension manually for paper trading. amount + (turbulence, turbulence_bool) + (price, shares, cd (holding time)) * stock_dim + tech_dim
+
+paper_trading_erl = PaperTradingAlpaca(
+    ticker_list=DOW_30_TICKER,
+    time_interval="1Min",
+    drl_lib="elegantrl",
+    agent="ppo",
+    cwd="./papertrading_erl_retrain",
+    net_dim=ERL_PARAMS["net_dimension"],
+    state_dim=state_dim,
+    action_dim=action_dim,
+    API_KEY=TRADING_API_KEY,
+    API_SECRET=TRADING_API_SECRET,
+    API_BASE_URL=TRADING_API_BASE_URL,
+    tech_indicator_list=INDICATORS,
+    turbulence_thresh=30,
+    max_stock=1e2,
+)
+
+paper_trading_erl.run()
+
+# Check Portfolio Performance
+# ## Get cumulative return
+df_erl, cumu_erl = alpaca_history(
+    key=DATA_API_KEY,
+    secret=DATA_API_SECRET,
+    url=DATA_API_BASE_URL,
+    start="2022-09-01",  # must be within 1 month
+    end="2022-09-12",
+)  # change the date if error occurs
+
+df_djia, cumu_djia = DIA_history(start="2022-09-01")
+returns_erl = cumu_erl - 1
+returns_dia = cumu_djia - 1
+returns_dia = returns_dia[: returns_erl.shape[0]]
+
+# plot and save
+import matplotlib.pyplot as plt
+
+plt.figure(dpi=1000)
+plt.grid()
+plt.grid(which="minor", axis="y")
+plt.title("Stock Trading (Paper trading)", fontsize=20)
+plt.plot(returns_erl, label="ElegantRL Agent", color="red")
+# plt.plot(returns_sb3, label = 'Stable-Baselines3 Agent', color = 'blue' )
+# plt.plot(returns_rllib, label = 'RLlib Agent', color = 'green')
+plt.plot(returns_dia, label="DJIA", color="grey")
+plt.ylabel("Return", fontsize=16)
+plt.xlabel("Year 2021", fontsize=16)
+plt.xticks(size=14)
+plt.yticks(size=14)
+ax = plt.gca()
+ax.xaxis.set_major_locator(ticker.MultipleLocator(78))
+ax.xaxis.set_minor_locator(ticker.MultipleLocator(6))
+ax.yaxis.set_minor_locator(ticker.MultipleLocator(0.005))
+ax.yaxis.set_major_formatter(ticker.PercentFormatter(xmax=1, decimals=2))
+ax.xaxis.set_major_formatter(
+    ticker.FixedFormatter(["", "10-19", "", "10-20", "", "10-21", "", "10-22"])
+)
+plt.legend(fontsize=10.5)
+plt.savefig("papertrading_stock.png")