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# Import necessary libraries
import streamlit as st
import requests
import pandas as pd
from datetime import datetime
from Pandas_Market_Predictor import Pandas_Market_Predictor
# Hard-coded API key for demonstration purposes
API_KEY = "QR8F9B7T6R2SWTAT"
def fetch_alpha_vantage_data(api_key, symbol):
try:
url = f'https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol={symbol}&interval=5min&apikey={api_key}'
response = requests.get(url)
response.raise_for_status() # Raise an error for bad responses
alpha_vantage_data = response.json()
return alpha_vantage_data
except requests.RequestException as e:
st.error(f"Error fetching data: {e}")
return None
def calculate_indicators(data):
# Convert all columns to numeric
data = data.apply(pd.to_numeric, errors='coerce')
# Example: Simple condition for doji and inside
data['Doji'] = abs(data['close'] - data['Open']) <= 0.01 * (data['High'] - data['Low'])
data['Inside'] = (data['High'] < data['High'].shift(1)) & (data['Low'] > data['Low'].shift(1))
return data
def display_signals(signal_type, signals):
st.subheader(f"{signal_type} Signals:")
st.write(signals)
def predict_trend(data):
# Create predictor
my_market_predictor = Pandas_Market_Predictor(data)
# Predict Trend
indicators = ["Doji", "Inside"]
# Display loading message during prediction
with st.spinner("Predicting trend using AI ...."):
# Predict trend
trend = my_market_predictor.Trend_Detection(indicators, 10)
return trend
def main():
st.title("Stock Trend Predictor")
# Input for stock symbol
symbol = st.text_input("Enter stock symbol (e.g., AAPL):", "AAPL")
# Fetch Alpha Vantage data
alpha_vantage_data = fetch_alpha_vantage_data(API_KEY, symbol)
if alpha_vantage_data:
# Extract relevant data from Alpha Vantage response
alpha_vantage_time_series = alpha_vantage_data.get('Time Series (5min)', {})
df = pd.DataFrame(alpha_vantage_time_series).T
df.index = pd.to_datetime(df.index)
df = df.dropna(axis=0)
# Rename columns
df = df.rename(columns={'1. open': 'Open', '2. high': 'High', '3. low': 'Low', '4. close': 'Close', '5. volume': 'Volume'})
# Calculate indicators
df = calculate_indicators(df)
# Display stock trading signals
strategic_signals = StrategicSignals(symbol=symbol)
# Display loading message during processing
with st.spinner("Predicting signals using Strategic Indicators..."):
# Display signals
st.subheader(":orange[Strategic Indicators Trend Prediction]")
display_signals("Bollinger Bands", strategic_signals.get_bollinger_bands_signals())
display_signals("Breakout", strategic_signals.get_breakout_signals())
display_signals("Crossover", strategic_signals.get_crossover_signals())
display_signals("MACD", strategic_signals.get_macd_signals())
display_signals("RSI", strategic_signals.get_rsi_signals())
# Predict trend using AI
trend = predict_trend(df)
# Display results
st.subheader(":orange[AI Trend Prediction]")
st.write("Buy Trend :", trend['BUY'])
st.write("Sell Trend :", trend['SELL'])
# Delete the DataFrame to release memory
del df
if __name__ == "__main__":
main() |