Visualize total profits

app.py

@@ -18,6 +18,11 @@ def get_time():
     return datetime.datetime.now().time()
 
 
+def get_profit():
+    global profit
+    return profit
+
+
 def pretrain_rl_agent():
     global equity
     observations = env_train.reset()
@@ -26,6 +31,7 @@ def pretrain_rl_agent():
         observations = torch.as_tensor(observations).float()
         action = agent(observations)
         observations, reward, _ = env_train.step(action.data.to("cpu").numpy())
+        reward *= 1e3
 
         action.backward()
 
@@ -92,7 +98,7 @@ data = data.tail(50000)
 data = data.set_index('Local time')
 date_split = '01.01.2023 16:04:00.000 GMT-0600'
 
-learning_rate = 0.001
+learning_rate = 0.01
 first_momentum = 0.0
 second_momentum = 0.0001
 transaction_cost = 0.0001
@@ -183,10 +189,10 @@ def trading_plot():
 # The UI of the demo defines here.
 with gr.Blocks() as demo:
     gr.Markdown("Auto trade bot.")
-    gr.Markdown(f"Profit: {profit}")
+    # gr.Markdown(f"Profit: {profit}")
 
-    # dt = gr.Textbox(label="Current time")
-    # demo.queue().load(get_time, inputs=None, outputs=dt, every=1)
+    dt = gr.Textbox(label="Total profit")
+    demo.queue().load(get_profit, inputs=None, outputs=dt, every=1)
 
     # for plotly it should follow this: https://gradio.app/plot-component-for-maps/
     candlestick_plot = gr.Plot().style()

rl_agent/env.py

@@ -37,16 +37,12 @@ class Environment:
         self.position_value = act
         
         self.history.pop(0)
-        
         self.history.append(self.data.iloc[self.t, :]['Close'] - self.data.iloc[(self.t-1), :]['Close']) # the price being traded
-        
         self.position_value = self.position_value.item()
 
         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'])