add all tutorials

Author: zhumingpassional

.DS_Store

@@ -0,0 +1,259 @@
+import warnings
+
+warnings.filterwarnings("ignore")
+
+import pandas as pd
+from IPython import display
+
+display.set_matplotlib_formats("svg")
+
+from meta import config
+from meta.data_processor import DataProcessor
+from main import check_and_make_directories
+from meta.data_processors.tushare import Tushare, ReturnPlotter
+from meta.env_stock_trading.env_stocktrading_China_A_shares import (
+    StockTradingEnv,
+)
+from agents.stablebaselines3_models import DRLAgent
+import os
+from typing import List
+from argparse import ArgumentParser
+from meta import config
+from meta.config_tickers import DOW_30_TICKER
+from meta.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,
+    ERL_PARAMS,
+    RLlib_PARAMS,
+    SAC_PARAMS,
+    ALPACA_API_KEY,
+    ALPACA_API_SECRET,
+    ALPACA_API_BASE_URL,
+)
+import pyfolio
+from pyfolio import timeseries
+
+pd.options.display.max_columns = None
+
+print("ALL Modules have been imported!")
+
+
+### Create folders
+
+import os
+
+"""
+use check_and_make_directories() to replace the following
+
+if not os.path.exists("./datasets"):
+    os.makedirs("./datasets")
+if not os.path.exists("./trained_models"):
+    os.makedirs("./trained_models")
+if not os.path.exists("./tensorboard_log"):
+    os.makedirs("./tensorboard_log")
+if not os.path.exists("./results"):
+    os.makedirs("./results")
+"""
+
+check_and_make_directories(
+    [DATA_SAVE_DIR, TRAINED_MODEL_DIR, TENSORBOARD_LOG_DIR, RESULTS_DIR]
+)
+
+
+### Download data, cleaning and feature engineering
+
+ticker_list = [
+    "600000.SH",
+    "600009.SH",
+    "600016.SH",
+    "600028.SH",
+    "600030.SH",
+    "600031.SH",
+    "600036.SH",
+    "600050.SH",
+    "600104.SH",
+    "600196.SH",
+    "600276.SH",
+    "600309.SH",
+    "600519.SH",
+    "600547.SH",
+    "600570.SH",
+]
+
+TRAIN_START_DATE = "2015-01-01"
+TRAIN_END_DATE = "2019-08-01"
+TRADE_START_DATE = "2019-08-01"
+TRADE_END_DATE = "2020-01-03"
+
+
+TIME_INTERVAL = "1d"
+kwargs = {}
+kwargs["token"] = "27080ec403c0218f96f388bca1b1d85329d563c91a43672239619ef5"
+p = DataProcessor(
+    data_source="tushare",
+    start_date=TRAIN_START_DATE,
+    end_date=TRADE_END_DATE,
+    time_interval=TIME_INTERVAL,
+    **kwargs,
+)
+
+
+# download and clean
+p.download_data(ticker_list=ticker_list)
+
+
+p.clean_data()
+
+
+# add_technical_indicator
+p.add_technical_indicator(config.INDICATORS)
+p.clean_data()
+print(f"p.dataframe: {p.dataframe}")
+
+
+### Split traning dataset
+
+train = p.data_split(p.dataframe, TRAIN_START_DATE, TRAIN_END_DATE)
+print(f"len(train.tic.unique()): {len(train.tic.unique())}")
+
+print(f"train.tic.unique(): {train.tic.unique()}")
+
+print(f"train.head(): {train.head()}")
+
+print(f"train.shape: {train.shape}")
+
+stock_dimension = len(train.tic.unique())
+state_space = stock_dimension * (len(config.INDICATORS) + 2) + 1
+print(f"Stock Dimension: {stock_dimension}, State Space: {state_space}")
+
+### Train
+
+env_kwargs = {
+    "stock_dim": stock_dimension,
+    "hmax": 1000,
+    "initial_amount": 1000000,
+    "buy_cost_pct": 6.87e-5,
+    "sell_cost_pct": 1.0687e-3,
+    "reward_scaling": 1e-4,
+    "state_space": state_space,
+    "action_space": stock_dimension,
+    "tech_indicator_list": config.INDICATORS,
+    "print_verbosity": 1,
+    "initial_buy": True,
+    "hundred_each_trade": True,
+}
+
+e_train_gym = StockTradingEnv(df=train, **env_kwargs)
+
+## DDPG
+
+env_train, _ = e_train_gym.get_sb_env()
+print(f"print(type(env_train)): {print(type(env_train))}")
+
+agent = DRLAgent(env=env_train)
+DDPG_PARAMS = {
+    "batch_size": 256,
+    "buffer_size": 50000,
+    "learning_rate": 0.0005,
+    "action_noise": "normal",
+}
+POLICY_KWARGS = dict(net_arch=dict(pi=[64, 64], qf=[400, 300]))
+model_ddpg = agent.get_model(
+    "ddpg", model_kwargs=DDPG_PARAMS, policy_kwargs=POLICY_KWARGS
+)
+
+trained_ddpg = agent.train_model(
+    model=model_ddpg, tb_log_name="ddpg", total_timesteps=10000
+)
+
+## A2C
+
+agent = DRLAgent(env=env_train)
+model_a2c = agent.get_model("a2c")
+
+trained_a2c = agent.train_model(
+    model=model_a2c, tb_log_name="a2c", total_timesteps=50000
+)
+
+### Trade
+
+trade = p.data_split(p.dataframe, TRADE_START_DATE, TRADE_END_DATE)
+env_kwargs = {
+    "stock_dim": stock_dimension,
+    "hmax": 1000,
+    "initial_amount": 1000000,
+    "buy_cost_pct": 6.87e-5,
+    "sell_cost_pct": 1.0687e-3,
+    "reward_scaling": 1e-4,
+    "state_space": state_space,
+    "action_space": stock_dimension,
+    "tech_indicator_list": config.INDICATORS,
+    "print_verbosity": 1,
+    "initial_buy": False,
+    "hundred_each_trade": True,
+}
+e_trade_gym = StockTradingEnv(df=trade, **env_kwargs)
+
+df_account_value, df_actions = DRLAgent.DRL_prediction(
+    model=trained_ddpg, environment=e_trade_gym
+)
+
+df_actions.to_csv("action.csv", index=False)
+print(f"df_actions: {df_actions}")
+
+### Backtest
+
+# matplotlib inline
+plotter = ReturnPlotter(df_account_value, trade, TRADE_START_DATE, TRADE_END_DATE)
+# plotter.plot_all()
+
+plotter.plot()
+
+# matplotlib inline
+# # ticket: SSE 50：000016
+# plotter.plot("000016")
+
+#### Use pyfolio
+
+# CSI 300
+baseline_df = plotter.get_baseline("399300")
+
+
+daily_return = plotter.get_return(df_account_value)
+daily_return_base = plotter.get_return(baseline_df, value_col_name="close")
+
+perf_func = timeseries.perf_stats
+perf_stats_all = perf_func(
+    returns=daily_return,
+    factor_returns=daily_return_base,
+    positions=None,
+    transactions=None,
+    turnover_denom="AGB",
+)
+print("==============DRL Strategy Stats===========")
+print(f"perf_stats_all: {perf_stats_all}")
+
+
+daily_return = plotter.get_return(df_account_value)
+daily_return_base = plotter.get_return(baseline_df, value_col_name="close")
+
+perf_func = timeseries.perf_stats
+perf_stats_all = perf_func(
+    returns=daily_return_base,
+    factor_returns=daily_return_base,
+    positions=None,
+    transactions=None,
+    turnover_denom="AGB",
+)
+print("==============Baseline Strategy Stats===========")
+
+print(f"perf_stats_all: {perf_stats_all}")