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import streamlit as st
import pandas as pd
import plotly.express as px
from data_loader import load_data
from indicators import calculate_indicators
from ai_insights import get_ai_insight
from portfolio_optimiser import optimize_portfolio
import os
# Streamlit UI
st.title("AI-Powered Stock Analysis and Portfolio Optimisation Tool")
# User Inputs
tickers = st.text_input("Enter stock tickers (comma-separated, e.g., TSLA, AAPL, MSFT):", "")
tickers = [ticker.strip().upper() for ticker in tickers.split(",")]
start_date = st.date_input("Start Date:", value=pd.to_datetime("2024-01-01"))
end_date = st.date_input("End Date:", value=pd.to_datetime("2025-01-01"))
ma_window = st.slider("Moving Average Window:", min_value=5, max_value=50, value=20)
rsi_window = st.slider("RSI Window:", min_value=5, max_value=30, value=14)
macd_fast = st.slider("MACD Fast Period:", min_value=5, max_value=30, value=12)
macd_slow = st.slider("MACD Slow Period:", min_value=20, max_value=50, value=26)
macd_signal = st.slider("MACD Signal Period:", min_value=5, max_value=20, value=9)
risk_tolerance = st.slider("Risk Tolerance (0-1):", min_value=0.0, max_value=1.0, value=0.5, step=0.01)
def calculate_and_return_everything(data):
# Calculate Indicators
indicator_data = calculate_indicators(data, ma_window, rsi_window, macd_fast, macd_slow, macd_signal)
if indicator_data is None:
st.stop()
# AI Insights
st.subheader("💡 AI Insights")
last_row = indicator_data.iloc[-1]
api_key = st.secrets["OPENAI_API_KEY"] # Ensure API key is set
# Get AI insights for each indicator
ma_insight = get_ai_insight("Moving Average", last_row['MA'], api_key)
bb_insight = get_ai_insight("Bollinger Bands", f"Upper: {last_row['Upper_BB']}, Lower: {last_row['Lower_BB']}", api_key)
rsi_insight = get_ai_insight("RSI", last_row['RSI'], api_key)
macd_insight = get_ai_insight("MACD", last_row['MACD'], api_key)
st.write(f"Moving Average: {ma_insight}")
st.write(f"Bollinger Bands: {bb_insight}")
st.write(f"RSI: {rsi_insight}")
st.write(f"MACD: {macd_insight}")
return indicator_data
if st.button("Analyse"):
all_data = {}
for i in tickers:
# Load Data
data = load_data(i, start_date, end_date) # Passing ticker instead of tickers
if data is None:
st.warning(f"No data found for {i}. Skipping...")
continue
st.subheader(f'Ticker: {i}')
id = calculate_and_return_everything(data)
if id is not None:
all_data[i] = id
if not all_data:
st.warning("No valid data available for any of the tickers. Exiting analysis.")
st.stop()
# Portfolio Optimisation
st.subheader("Portfolio Optimisation")
# Extract closing prices for portfolio optimization
close_prices = pd.DataFrame({ticker: all_data[ticker]['Close'] for ticker in all_data})
print(close_prices.columns)
asset_weights = optimize_portfolio(close_prices, risk_tolerance)
if asset_weights:
st.write("### Optimised Asset Allocation:")
for ticker, weight in asset_weights.items():
st.write(f"**{ticker}:** {weight:.4f}")
else:
st.write("Could not perform portfolio optimisation.")
print("Ensure that you have your OpenAI API set as environmental variable in Hugging Face Spaces for this script to work")
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