Delete app.py

app.py

@@ -1,103 +0,0 @@
-# Required imports
-import yfinance as yf
-import pandas as pd
-import numpy as np
-from scipy.signal import find_peaks
-import plotly.graph_objects as go
-import plotly.express as px
-import streamlit as st
-from plotly.subplots import make_subplots
-
-# Define functions for technical indicators
-def compute_macd(data, slow=26, fast=12, smooth=9):
-    exp1 = data['Close'].ewm(span=fast, adjust=False).mean()
-    exp2 = data['Close'].ewm(span=slow, adjust=False).mean()
-    macd = exp1 - exp2
-    signal = macd.ewm(span=smooth, adjust=False).mean()
-    return macd, signal
-
-def compute_rsi(data, window=14):
-    delta = data['Close'].diff()
-    gain = (delta.where(delta > 0, 0)).rolling(window=window).mean()
-    loss = (-delta.where(delta < 0, 0)).rolling(window=window).mean()
-    rs = gain / loss
-    return 100 - (100 / (1 + rs))
-
-def compute_bollinger(data, window=20, num_std=2):
-    rolling_mean = data['Close'].rolling(window=window).mean()
-    rolling_std = data['Close'].rolling(window=window).std()
-    upper_band = rolling_mean + (rolling_std * num_std)
-    lower_band = rolling_mean - (rolling_std * num_std)
-    return upper_band, lower_band
-
-# Streamlit UI - Introduction and How to Use the App
-st.title("Netflyp Stock and Crypto Analysis Tool")
-st.markdown("""
-Welcome to Netflyp Stock and Crypto Analysis Tool. This version includes RSI, MACD, Bollinger Bands, and volume indicators.
-
-**Features**:
-- View stock and crypto price movements and indicators over time.
-- Analyze technical indicators such as RSI, MACD, and Bollinger Bands.
-- Identify and visualize MA crossovers which are significant for trading strategies.
-- Download charts for offline analysis.
-
-**Instructions**:
-1. Enter a stock or crypto symbol (as they are in yahoo finance) in the sidebar or choose from sample tickers below.
-2. Select your desired time period.
-3. Click the 'Analyze' button to load and display the data.
-""")
-
-# User Inputs
-sidebar = st.sidebar
-symbol = sidebar.text_input("Enter stock or crypto symbol", "AAPL")
-period = sidebar.selectbox("Select period", ["1mo", "3mo", "6mo", "1y", "2y", "5y", "10y", "ytd", "max"])
-
-# Sample Inputs
-sample_tickers = ["NVDA", "BTC-USD", "5258.KL", "5209.KL", "5235SS.KL"]
-ticker_buttons = st.radio("Sample tickers:", sample_tickers)
-
-# Set ticker from sample when clicked
-if ticker_buttons:
-    symbol = ticker_buttons
-    sidebar.text_input("Enter stock symbol", value=symbol, key="1")
-
-# Button to trigger analysis
-if st.button('Analyze') or st.sidebar.button('Analyze Sidebar'):
-    # Fetch stock data
-    data = yf.download(symbol, period=period)
-
-    # Calculate technical indicators
-    data['MA20'] = data['Close'].rolling(window=20).mean()
-    data['MA50'] = data['Close'].rolling(window=50).mean()
-    data['MA200'] = data['Close'].rolling(window=200).mean()
-    data['RSI'] = compute_rsi(data)
-    data['MACD'], data['Signal'] = compute_macd(data)
-    data['Upper_Band'], data['Lower_Band'] = compute_bollinger(data)
-
-    # Plot setup
-    fig = make_subplots(rows=3, cols=1, shared_xaxes=True, vertical_spacing=0.02,
-                        subplot_titles=('Stock Price and Moving Averages', 'Volume and Bollinger Bands', 'RSI and MACD'))
-
-    # Add traces
-    fig.add_trace(go.Scatter(x=data.index, y=data['Close'], name='Close Price'), row=1, col=1)
-    fig.add_trace(go.Scatter(x=data.index, y=data['MA20'], name='20-Period MA', line=dict(color='green')), row=1, col=1)
-    fig.add_trace(go.Scatter(x=data.index, y=data['MA50'], name='50-Period MA', line=dict(color='blue')), row=1, col=1)
-    fig.add_trace(go.Scatter(x=data.index, y=data['MA200'], name='200-Period MA', line=dict(color='red')), row=1, col=1)
-    fig.add_trace(go.Bar(x=data.index, y=data['Volume'], name='Volume'), row=2, col=1)
-    fig.add_trace(go.Scatter(x=data.index, y=data['Upper_Band'], name='Upper Bollinger Band', line=dict(color='purple')), row=2, col=1)
-    fig.add_trace(go.Scatter(x=data.index, y=data['Lower_Band'], name='Lower Bollinger Band', line=dict(color='purple')), row=2, col=1)
-    fig.add_trace(go.Scatter(x=data.index, y=data['RSI'], name='RSI', line=dict(color='orange')), row=3, col=1)
-    fig.add_trace(go.Scatter(x=data.index, y=data['MACD'], name='MACD', line=dict(color='pink')), row=3, col=1)
-    fig.add_trace(go.Scatter(x=data.index, y=data['Signal'], name='MACD Signal', line=dict(color='cyan')), row=3, col=1)
-
-    # Layout adjustments
-    fig.update_layout(height=1200, width=800, showlegend=True)
-    fig.update_yaxes(title_text="Price", row=1, col=1)
-    fig.update_yaxes(title_text="Volume", row=2, col=1)
-    fig.update_yaxes(title_text="Index Value", row=3, col=1)
-
-    # Display the chart with a download button
-    st.plotly_chart(fig)
-    fig.write_html("stock_analysis_chart.html")
-    st.markdown("[Download Chart](stock_analysis_chart.html)")
-