add env

rl_agent/env.py

@@ -0,0 +1,72 @@
+import numpy as np
+import pandas as pd
+
+class Environment:
+    
+    def __init__(self, data, history_t=90):
+        self.data = data
+        self.history_t = history_t
+        self.reset()
+        
+    def reset(self):
+        self.t = 0
+        self.done = False
+        self.profits = 0
+        self.positions = []
+        self.position_value = 0
+        self.history = [0 for _ in range(self.history_t)]
+        return [self.position_value] + self.history # obs
+    
+    def step(self, act):
+        reward = 0
+        
+        # act = 0: stay, 1: buy, -1: sell
+        if act == 1:
+            self.positions.append(self.data.iloc[self.t, :]['Close'])
+        elif act == 2: # sell
+            if len(self.positions) == 0:
+                reward = -1
+            else:
+                profits = 0
+                for p in self.positions:
+                    profits += (self.data.iloc[self.t, :]['Close'] - p)
+                reward += profits
+                self.profits += profits
+                self.positions = []
+        
+        # set next time
+        self.t += 1
+        self.position_value = 0
+        for p in self.positions:
+            self.position_value += (self.data.iloc[self.t, :]['Close'] - p)
+        self.history.pop(0)
+        self.history.append(self.data.iloc[self.t, :]['Close'] - self.data.iloc[(self.t-1), :]['Close'])
+        
+        # clipping reward
+        if reward > 0:
+            reward = 1
+        elif reward < 0:
+            reward = -1
+        
+        return [self.position_value] + self.history, reward, self.done # obs, reward, done
+
+
+
+
+if __name__ == "__main__":
+    data = pd.read_csv('./data/EURUSD_Candlestick_1_M_BID_01.01.2021-04.02.2023.csv')
+    # data['Local time'] = pd.to_datetime(data['Local time'])
+    data = data.set_index('Local time')
+    print(data.index.min(), data.index.max())
+
+    date_split = '19.09.2022 17:55:00.000 GMT-0500'
+    train = data[:date_split]
+    test = data[date_split:]
+    print(train.head(10))
+
+    env = Environment(train)
+    print(env.reset())
+    for _ in range(3):
+        pact = np.random.randint(3)
+        print(env.step(pact))
+