Update app.py

app.py

@@ -2,7 +2,6 @@ import streamlit as st
 import requests
 from Pandas_Market_Predictor import Pandas_Market_Predictor
 import pandas as pd
-import numpy as np
 
 # Hard-coded API key for demonstration purposes
 API_KEY = "QR8F9B7T6R2SWTAT"
@@ -13,19 +12,6 @@ def fetch_alpha_vantage_data(api_key):
     alpha_vantage_data = response.json()
     return alpha_vantage_data
 
-def calculate_ichimoku_cloud(data):
-    short_window = 9
-    long_window = 26
-    span_b_window = 52
-    displacement = 26
-
-    data['tenkan_sen'] = (data['high'].rolling(window=short_window).max() + data['low'].rolling(window=short_window).min()) / 2
-    data['kijun_sen'] = (data['high'].rolling(window=long_window).max() + data['low'].rolling(window=long_window).min()) / 2
-    data['senkou_span_a'] = ((data['tenkan_sen'] + data['kijun_sen']) / 2).shift(displacement)
-    data['senkou_span_b'] = ((data['high'].rolling(window=span_b_window).max() + data['low'].rolling(window=span_b_window).min()) / 2).shift(displacement)
-
-    return data  # Make sure to return the updated DataFrame
-
 def calculate_indicators(data):
     data = data.apply(pd.to_numeric, errors='coerce')
 
@@ -39,9 +25,6 @@ def calculate_indicators(data):
     data['12EMA'] = data['Close'].ewm(span=12).mean()
     data['MACD'] = data['12EMA'] - data['26EMA']
 
-    # Calculate Ichimoku Cloud
-    data = calculate_ichimoku_cloud(data)
-
     return data
 
 def main():
@@ -62,13 +45,11 @@ def main():
 
     my_market_predictor = Pandas_Market_Predictor(df)
 
-    # Print data for each indicator before making predictions
-    for indicator in ["THR", "LGR", "THd", "THM"]:
-        indicator_data = my_market_predictor.get_indicator_data(indicator)
-        print(f"Data for {indicator}:")
-        print(indicator_data.head())  # Print the first few rows of data for the indicator
+    # Remove Ichimoku Cloud columns
+    ichimoku_columns = ["tenkan_sen", "kijun_sen", "senkou_span_a", "senkou_span_b"]
+    df = df.drop(columns=ichimoku_columns)
 
-    indicators = ["Doji", "Inside", "MA5", "MA20", "MACD", "tenkan_sen", "kijun_sen", "senkou_span_a", "senkou_span_b"]
+    indicators = ["Doji", "Inside", "MA5", "MA20", "MACD"]
     trend = my_market_predictor.Trend_Detection(indicators, 10)
 
     st.subheader("Predicted Trend:")