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RL-Bitcoin-trading-bot_1/RL-Bitcoin-trading-bot_1.py

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+#================================================================
+#
+#   File name   : RL-Bitcoin-trading-bot_1.py
+#   Author      : PyLessons
+#   Created date: 2020-12-02
+#   Website     : https://pylessons.com/
+#   GitHub      : https://github.com/pythonlessons/RL-Bitcoin-trading-bot
+#   Description : Introduction to trading Crypto with Reinforcement Learning
+#
+#================================================================
+import pandas as pd
+import numpy as np
+import random
+from collections import deque
+
+class CustomEnv:
+    # A custom Bitcoin trading environment
+    def __init__(self, df, initial_balance=1000, lookback_window_size=50):
+        # Define action space and state size and other custom parameters
+        self.df = df.dropna().reset_index()
+        self.df_total_steps = len(self.df)-1
+        self.initial_balance = initial_balance
+        self.lookback_window_size = lookback_window_size
+
+        # Action space from 0 to 3, 0 is hold, 1 is buy, 2 is sell
+        self.action_space = np.array([0, 1, 2])
+
+        # Orders history contains the balance, net_worth, crypto_bought, crypto_sold, crypto_held values for the last lookback_window_size steps
+        self.orders_history = deque(maxlen=self.lookback_window_size)
+        
+        # Market history contains the OHCL values for the last lookback_window_size prices
+        self.market_history = deque(maxlen=self.lookback_window_size)
+
+        # State size contains Market+Orders history for the last lookback_window_size steps
+        self.state_size = (self.lookback_window_size, 10)
+
+    # Reset the state of the environment to an initial state
+    def reset(self, env_steps_size = 0):
+        self.balance = self.initial_balance
+        self.net_worth = self.initial_balance
+        self.prev_net_worth = self.initial_balance
+        self.crypto_held = 0
+        self.crypto_sold = 0
+        self.crypto_bought = 0
+        if env_steps_size > 0: # used for training dataset
+            self.start_step = random.randint(self.lookback_window_size, self.df_total_steps - env_steps_size)
+            self.end_step = self.start_step + env_steps_size
+        else: # used for testing dataset
+            self.start_step = self.lookback_window_size
+            self.end_step = self.df_total_steps
+            
+        self.current_step = self.start_step
+
+        for i in reversed(range(self.lookback_window_size)):
+            current_step = self.current_step - i
+            self.orders_history.append([self.balance, self.net_worth, self.crypto_bought, self.crypto_sold, self.crypto_held])
+            self.market_history.append([self.df.loc[current_step, 'Open'],
+                                        self.df.loc[current_step, 'High'],
+                                        self.df.loc[current_step, 'Low'],
+                                        self.df.loc[current_step, 'Close'],
+                                        self.df.loc[current_step, 'Volume']
+                                        ])
+
+        state = np.concatenate((self.market_history, self.orders_history), axis=1)
+        return state
+
+    # Get the data points for the given current_step
+    def _next_observation(self):
+        self.market_history.append([self.df.loc[self.current_step, 'Open'],
+                                    self.df.loc[self.current_step, 'High'],
+                                    self.df.loc[self.current_step, 'Low'],
+                                    self.df.loc[self.current_step, 'Close'],
+                                    self.df.loc[self.current_step, 'Volume']
+                                    ])
+        obs = np.concatenate((self.market_history, self.orders_history), axis=1)
+        return obs
+
+    # Execute one time step within the environment
+    def step(self, action):
+        self.crypto_bought = 0
+        self.crypto_sold = 0
+        self.current_step += 1
+
+        # Set the current price to a random price between open and close
+        current_price = random.uniform(
+            self.df.loc[self.current_step, 'Open'],
+            self.df.loc[self.current_step, 'Close'])
+        
+        if action == 0: # Hold
+            pass
+        
+        elif action == 1 and self.balance > 0:
+            # Buy with 100% of current balance
+            self.crypto_bought = self.balance / current_price
+            self.balance -= self.crypto_bought * current_price
+            self.crypto_held += self.crypto_bought
+
+        elif action == 2 and self.crypto_held>0:
+            # Sell 100% of current crypto held
+            self.crypto_sold = self.crypto_held
+            self.balance += self.crypto_sold * current_price
+            self.crypto_held -= self.crypto_sold
+
+        self.prev_net_worth = self.net_worth
+        self.net_worth = self.balance + self.crypto_held * current_price
+
+        self.orders_history.append([self.balance, self.net_worth, self.crypto_bought, self.crypto_sold, self.crypto_held])
+
+        # Calculate reward
+        reward = self.net_worth - self.prev_net_worth
+
+        if self.net_worth <= self.initial_balance/2:
+            done = True
+        else:
+            done = False
+
+        obs = self._next_observation()
+        
+        return obs, reward, done
+
+    # render environment
+    def render(self):
+        print(f'Step: {self.current_step}, Net Worth: {self.net_worth}')
+
+        
+def Random_games(env, train_episodes = 50, training_batch_size=500):
+    average_net_worth = 0
+    for episode in range(train_episodes):
+        state = env.reset(env_steps_size = training_batch_size)
+
+        while True:
+            env.render()
+
+            action = np.random.randint(3, size=1)[0]
+
+            state, reward, done = env.step(action)
+
+            if env.current_step == env.end_step:
+                average_net_worth += env.net_worth
+                print("net_worth:", env.net_worth)
+                break
+
+    print("average_net_worth:", average_net_worth/train_episodes)
+
+
+df = pd.read_csv('./pricedata.csv')
+df = df.sort_values('Date')
+
+lookback_window_size = 10
+train_df = df[:-720-lookback_window_size]
+test_df = df[-720-lookback_window_size:] # 30 days
+
+train_env = CustomEnv(train_df, lookback_window_size=lookback_window_size)
+test_env = CustomEnv(test_df, lookback_window_size=lookback_window_size)
+
+Random_games(train_env, train_episodes = 10, training_batch_size=500)