import streamlit as st
import pandas as pd
import numpy as np
import yfinance as yf
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import warnings
warnings.filterwarnings('ignore')
from curl_cffi import requests
session = requests.Session(impersonate="chrome")
# Import all technical indicators from your file
from technical_indicators import *
# Page configuration
st.set_page_config(
page_title="Technical Analysis Dashboard",
page_icon="📈",
layout="wide",
initial_sidebar_state="expanded"
)
# Custom CSS for better styling
st.markdown("""
""", unsafe_allow_html=True)
# Title
st.markdown('📈 Technical Analysis Dashboard
', unsafe_allow_html=True)
st.markdown('', unsafe_allow_html=True)
st.divider()
# Sidebar for inputs
with st.sidebar:
st.header("📊 Configuration")
# Stock symbol input
symbol = st.text_input("Stock Symbol", value="AAPL", help="Enter stock symbol (e.g., AAPL, GOOGL, MSFT)")
# Time period selection
period = st.selectbox(
"Time Period",
["1mo", "3mo", "6mo", "1y", "2y", "5y", "max"],
index=3
)
# Interval selection
interval = st.selectbox(
"Data Interval",
["1d", "5d", "1wk", "1mo"],
index=0
)
st.divider()
# Indicator Categories
st.header("📈 Select Indicators")
# Trend Indicators
with st.expander("Trend Indicators", expanded=True):
show_sma = st.checkbox("Simple Moving Average (SMA)", value=True)
show_ema = st.checkbox("Exponential Moving Average (EMA)", value=True)
show_hma = st.checkbox("Hull Moving Average (HMA)")
show_wma = st.checkbox("Weighted Moving Average (WMA)")
show_kama = st.checkbox("Kaufman Adaptive Moving Average (KAMA)")
show_frama = st.checkbox("Fractal Adaptive Moving Average (FRAMA)")
show_evwma = st.checkbox("Ehlers Volatility Weighted MA (EVWMA)")
show_vwap = st.checkbox("Volume Weighted Average Price (VWAP)")
# Momentum Indicators
with st.expander("Momentum Indicators", expanded=True):
show_rsi = st.checkbox("Relative Strength Index (RSI)", value=True)
show_macd = st.checkbox("MACD", value=True)
show_stochrsi = st.checkbox("Stochastic RSI")
show_cmo = st.checkbox("Chande Momentum Oscillator (CMO)")
show_roc = st.checkbox("Rate of Change (ROC)")
show_tsi = st.checkbox("True Strength Index (TSI)")
show_kst = st.checkbox("Know Sure Thing (KST)")
show_ppo = st.checkbox("Price Percentage Oscillator (PPO)")
show_uo = st.checkbox("Ultimate Oscillator (UO)")
# Volume Indicators
with st.expander("Volume Indicators"):
show_obv = st.checkbox("On-Balance Volume (OBV)")
show_adl = st.checkbox("Accumulation/Distribution Line (ADL)")
show_chaikin = st.checkbox("Chaikin Oscillator")
show_efi = st.checkbox("Elder's Force Index (EFI)")
show_emv = st.checkbox("Ease of Movement (EMV)")
show_mfi = st.checkbox("Money Flow Index (MFI)")
show_vpt = st.checkbox("Volume Price Trend (VPT)")
show_fve = st.checkbox("Fractal Volume Efficiency (FVE)")
show_vzo = st.checkbox("Volume Zone Oscillator (VZO)")
# Volatility Indicators
with st.expander("Volatility Indicators"):
show_bollinger = st.checkbox("Bollinger Bands", value=True)
show_kc = st.checkbox("Keltner Channels")
show_dc = st.checkbox("Donchian Channels")
show_atr = st.checkbox("Average True Range (ATR)")
show_chandelier = st.checkbox("Chandelier Exit")
show_psar = st.checkbox("Parabolic SAR")
show_apz = st.checkbox("Adaptive Price Zone (APZ)")
# Oscillators
with st.expander("Oscillators"):
show_adx = st.checkbox("Average Directional Index (ADX)")
show_cci = st.checkbox("Commodity Channel Index (CCI)")
show_fish = st.checkbox("Fisher Transform")
show_ao = st.checkbox("Awesome Oscillator (AO)")
show_mi = st.checkbox("Mass Index (MI)")
show_wto = st.checkbox("Wave Trend Oscillator (WTO)")
show_copp = st.checkbox("Coppock Curve")
show_ift_rsi = st.checkbox("Inverse Fisher Transform RSI")
# Complex Indicators
with st.expander("Complex Indicators"):
show_ichimoku = st.checkbox("Ichimoku Cloud")
show_pivot = st.checkbox("Pivot Points")
show_pivot_fib = st.checkbox("Fibonacci Pivot Points")
show_basp = st.checkbox("Buyer and Seller Pressure (BASP)")
show_baspn = st.checkbox("Normalized BASP")
show_dmi = st.checkbox("Directional Movement Index (DMI)")
show_ebbp = st.checkbox("Elder Bull/Bear Power")
st.divider()
# Parameter settings
st.header("⚙️ Parameters")
sma_period = st.slider("SMA Period", 5, 50, 20)
ema_period = st.slider("EMA Period", 5, 50, 20)
rsi_period = st.slider("RSI Period", 5, 30, 14)
bb_period = st.slider("Bollinger Bands Period", 10, 30, 20)
bb_std = st.slider("Bollinger Bands Std Dev", 1.0, 3.0, 2.0, 0.1)
col1, col2, col3, col4, col5 = st.columns(5)
st.divider()
@st.cache_data
def fetch_data(symbol, period, interval):
ticker = yf.Ticker(symbol.upper(), session=session)
return ticker.history(period=period, interval=interval)
# Main content area
if col3.button("🚀 Analyze Stock", type="secondary", use_container_width=True):
try:
# Fetch data
with st.spinner(f"Fetching data for {symbol.upper()}..."):
data = fetch_data(symbol, period, interval)
if data.empty:
st.error("No data found for the given symbol. Please check the symbol and try again.")
st.stop()
# Display basic info
col1, col2, col3, col4 = st.columns(4)
with col1:
st.metric("Current Price", f"${data['Close'].iloc[-1]:.2f}")
with col2:
price_change = data['Close'].iloc[-1] - data['Close'].iloc[-2]
st.metric("Price Change", f"${price_change:.2f}", f"{price_change:.2f}")
with col3:
pct_change = (price_change / data['Close'].iloc[-2]) * 100
st.metric("% Change", f"{pct_change:.2f}%", f"{pct_change:.2f}%")
with col4:
st.metric("Volume", f"{data['Volume'].iloc[-1]:,.0f}")
# Calculate indicators
indicators = {}
# Trend Indicators
if show_sma:
indicators['SMA'] = SMA(data, sma_period)
if show_ema:
indicators['EMA'] = EMA(data, ema_period)
if show_hma:
indicators['HMA'] = HMA(data, 20)
if show_wma:
indicators['WMA'] = WMA(data, 20)
if show_kama:
indicators['KAMA'] = KAMA(data)
if show_frama:
indicators['FRAMA'] = FRAMA(data)
if show_evwma:
indicators['EVWMA'] = EVWMA(data)
if show_vwap:
indicators['VWAP'] = VWAP(data)
# Momentum Indicators
if show_rsi:
indicators['RSI'] = RSI(data, rsi_period)
if show_macd:
indicators['MACD'] = MACD(data)
if show_stochrsi:
indicators['StochRSI'] = STOCHRSI(data)
if show_cmo:
indicators['CMO'] = CMO(data)
if show_roc:
indicators['ROC'] = ROC(data)
if show_tsi:
indicators['TSI'] = TSI(data)
if show_kst:
indicators['KST'] = KST(data)
if show_ppo:
indicators['PPO'] = PPO(data)
if show_uo:
indicators['UO'] = UO(data)
# Volume Indicators
if show_obv:
indicators['OBV'] = OBV(data)
if show_adl:
indicators['ADL'] = ADL(data)
if show_chaikin:
indicators['Chaikin'] = CHAIKIN(data)
if show_efi:
indicators['EFI'] = EFI(data)
if show_emv:
indicators['EMV'] = EMV(data)
if show_mfi:
indicators['MFI'] = MFI(data)
if show_vpt:
indicators['VPT'] = VPT(data)
if show_fve:
indicators['FVE'] = FVE(data)
if show_vzo:
indicators['VZO'] = VZO(data)
# Volatility Indicators
if show_bollinger:
indicators['Bollinger'] = BOLLINGER(data, bb_period, bb_std)
if show_kc:
indicators['KC'] = KC(data)
if show_dc:
indicators['DC'] = DC(data)
if show_atr:
indicators['ATR'] = ATR(data)
if show_chandelier:
indicators['Chandelier'] = CHANDELIER(data)
if show_psar:
indicators['PSAR'] = PSAR(data)
if show_apz:
indicators['APZ'] = APZ(data)
# Oscillators
if show_adx:
indicators['ADX'] = ADX(data)
if show_cci:
indicators['CCI'] = CCI(data)
if show_fish:
indicators['Fisher'] = FISH(data)
if show_ao:
indicators['AO'] = AO(data)
if show_mi:
indicators['MI'] = MI(data)
if show_wto:
indicators['WTO'] = WTO(data)
if show_copp:
indicators['Coppock'] = COPP(data)
if show_ift_rsi:
indicators['IFT_RSI'] = IFT_RSI(data)
# Complex Indicators
if show_ichimoku:
indicators['Ichimoku'] = ICHIMOKU(data)
if show_pivot:
indicators['Pivot'] = PIVOT(data)
if show_pivot_fib:
indicators['Pivot_Fib'] = PIVOT_FIB(data)
if show_basp:
indicators['BASP'] = BASP(data)
if show_baspn:
indicators['BASPN'] = BASPN(data)
if show_dmi:
indicators['DMI'] = DMI(data)
if show_ebbp:
indicators['EBBP'] = EBBP(data)
# Create main price chart
fig = make_subplots(
rows=4, cols=1,
shared_xaxes=True,
vertical_spacing=0.05,
subplot_titles=('Price Chart', 'Volume', 'Oscillators', 'Additional Indicators'),
row_heights=[0.5, 0.2, 0.15, 0.15]
)
# Add candlestick chart
fig.add_trace(
go.Candlestick(
x=data.index,
open=data['Open'],
high=data['High'],
low=data['Low'],
close=data['Close'],
name='Price'
),
row=1, col=1
)
# Define colors for trend indicators to avoid repetition
colors = ["red", "yellow", "green", "purple", "orange", "brown", "pink", "gray", "cyan", "magenta"]
color_idx = 0
# Add trend indicators to price chart (row 1)
trend_indicators = ['SMA', 'EMA', 'HMA', 'WMA', 'KAMA', 'FRAMA', 'EVWMA', 'VWAP']
for name in trend_indicators:
if name in indicators:
fig.add_trace(
go.Scatter(
x=data.index,
y=indicators[name].fillna(method='ffill'), # Handle NaNs
mode='lines',
name=name,
line=dict(color=colors[color_idx % len(colors)])
),
row=1, col=1
)
color_idx += 1
# Add volatility indicators to price chart (row 1)
if 'Bollinger' in indicators:
bb = indicators['Bollinger']
fig.add_trace(
go.Scatter(
x=data.index,
y=bb['BB_UPPER'].fillna(method='ffill'),
mode='lines',
name='BB Upper',
line=dict(color='lightblue', dash='dash')
),
row=1, col=1
)
fig.add_trace(
go.Scatter(
x=data.index,
y=bb['BB_LOWER'].fillna(method='ffill'),
mode='lines',
name='BB Lower',
line=dict(color='lightblue', dash='dash'),
fill='tonexty',
fillcolor='rgba(173, 216, 230, 0.2)'
),
row=1, col=1
)
if 'KC' in indicators:
kc = indicators['KC']
fig.add_trace(
go.Scatter(x=data.index, y=kc['KC_UPPER'].fillna(method='ffill'), name='KC Upper', line=dict(color='orange')),
row=1, col=1
)
fig.add_trace(
go.Scatter(x=data.index, y=kc['KC_LOWER'].fillna(method='ffill'), name='KC Lower', line=dict(color='orange')),
row=1, col=1
)
fig.add_trace(
go.Scatter(x=data.index, y=kc['KC_MIDDLE'].fillna(method='ffill'), name='KC Middle', line=dict(color='gray', dash='dot')),
row=1, col=1
)
if 'DC' in indicators:
dc = indicators['DC']
fig.add_trace(
go.Scatter(x=data.index, y=dc['DC_U'].fillna(method='ffill'), name='DC Upper', line=dict(color='green')),
row=1, col=1
)
fig.add_trace(
go.Scatter(x=data.index, y=dc['DC_L'].fillna(method='ffill'), name='DC Lower', line=dict(color='green')),
row=1, col=1
)
fig.add_trace(
go.Scatter(x=data.index, y=dc['DC_M'].fillna(method='ffill'), name='DC Middle', line=dict(color='limegreen', dash='dot')),
row=1, col=1
)
if 'Chandelier' in indicators:
ce = indicators['Chandelier']
fig.add_trace(
go.Scatter(x=data.index, y=ce['CHANDELIER_Long'].fillna(method='ffill'), name='Chandelier Long', line=dict(color='darkred')),
row=1, col=1
)
fig.add_trace(
go.Scatter(x=data.index, y=ce['CHANDELIER_Short'].fillna(method='ffill'), name='Chandelier Short', line=dict(color='darkgreen')),
row=1, col=1
)
if 'APZ' in indicators:
apz = indicators['APZ']
fig.add_trace(
go.Scatter(x=data.index, y=apz['APZ_UPPER'].fillna(method='ffill'), name='APZ Upper', line=dict(color='orange', dash='dot')),
row=1, col=1
)
fig.add_trace(
go.Scatter(x=data.index, y=apz['APZ_LOWER'].fillna(method='ffill'), name='APZ Lower', line=dict(color='coral', dash='dot')),
row=1, col=1
)
if 'Ichimoku' in indicators:
ichimoku = indicators['Ichimoku']
fig.add_trace(
go.Scatter(x=data.index, y=ichimoku['TENKAN'].fillna(method='ffill'), name='Tenkan-sen', line=dict(color='blue')),
row=1, col=1
)
fig.add_trace(
go.Scatter(x=data.index, y=ichimoku['KIJUN'].fillna(method='ffill'), name='Kijun-sen', line=dict(color='red')),
row=1, col=1
)
fig.add_trace(
go.Scatter(x=data.index, y=ichimoku['SENKOU_A'].fillna(method='ffill'), name='Senkou A', line=dict(color='green')),
row=1, col=1
)
fig.add_trace(
go.Scatter(x=data.index, y=ichimoku['SENKOU_B'].fillna(method='ffill'), name='Senkou B', line=dict(color='red'), fill='tonexty', fillcolor='rgba(0, 255, 0, 0.2)'),
row=1, col=1
)
fig.add_trace(
go.Scatter(x=data.index, y=ichimoku['CHIKOU'].fillna(method='ffill'), name='Chikou Span', line=dict(color='purple')),
row=1, col=1
)
if 'Pivot' in indicators:
pivot = indicators['Pivot']
for col in ['pivot', 'r1', 'r2', 'r3', 's1', 's2', 's3']:
fig.add_trace(
go.Scatter(x=data.index, y=pivot[col].fillna(method='ffill'), name=f'Pivot {col.upper()}', line=dict(dash='dash')),
row=1, col=1
)
if 'Pivot_Fib' in indicators:
pivot_fib = indicators['Pivot_Fib']
for col in ['pivot', 'r1', 'r2', 'r3', 's1', 's2', 's3']:
fig.add_trace(
go.Scatter(x=data.index, y=pivot_fib[col].fillna(method='ffill'), name=f'Fib Pivot {col.upper()}', line=dict(dash='dot')),
row=1, col=1
)
if 'PSAR' in indicators:
psar = indicators['PSAR']
fig.add_trace(
go.Scatter(x=data.index, y=psar['psar'].fillna(method='ffill'), name='PSAR', mode='markers', marker=dict(size=5, color='blue')),
row=1, col=1
)
fig.add_trace(
go.Scatter(x=data.index, y=psar['psarbull'].fillna(method='ffill'), name='PSAR Bull', mode='markers', marker=dict(size=5, color='green')),
row=1, col=1
)
fig.add_trace(
go.Scatter(x=data.index, y=psar['psarbear'].fillna(method='ffill'), name='PSAR Bear', mode='markers', marker=dict(size=5, color='red')),
row=1, col=1
)
# Add volume (row 2)
fig.add_trace(
go.Bar(
x=data.index,
y=data['Volume'],
name='Volume',
marker_color='lightblue'
),
row=2, col=1
)
# Add oscillators to row 3
if 'RSI' in indicators:
fig.add_trace(
go.Scatter(x=data.index, y=indicators['RSI'].fillna(method='ffill'), mode='lines', name='RSI', line=dict(color='purple')),
row=3, col=1
)
fig.add_hline(y=70, line_dash="dash", line_color="red", row=3, col=1)
fig.add_hline(y=30, line_dash="dash", line_color="green", row=3, col=1)
if 'StochRSI' in indicators:
fig.add_trace(
go.Scatter(x=data.index, y=indicators['StochRSI'].fillna(method='ffill'), mode='lines', name='StochRSI', line=dict(color='orange')),
row=3, col=1
)
fig.add_hline(y=80, line_dash="dash", line_color="red", row=3, col=1)
fig.add_hline(y=20, line_dash="dash", line_color="green", row=3, col=1)
if 'CCI' in indicators:
fig.add_trace(
go.Scatter(x=data.index, y=indicators['CCI'].fillna(method='ffill'), mode='lines', name='CCI', line=dict(color='blue')),
row=3, col=1
)
fig.add_hline(y=100, line_dash="dash", line_color="red", row=3, col=1)
fig.add_hline(y=-100, line_dash="dash", line_color="green", row=3, col=1)
if 'ADX' in indicators:
fig.add_trace(
go.Scatter(x=data.index, y=indicators['ADX'].fillna(method='ffill'), mode='lines', name='ADX', line=dict(color='cyan')),
row=3, col=1
)
fig.add_hline(y=25, line_dash="dash", line_color="gray", row=3, col=1)
if 'Fisher' in indicators:
fig.add_trace(
go.Scatter(x=data.index, y=indicators['Fisher'].fillna(method='ffill'), mode='lines', name='Fisher Transform', line=dict(color='magenta')),
row=3, col=1
)
if 'AO' in indicators:
fig.add_trace(
go.Scatter(x=data.index, y=indicators['AO'].fillna(method='ffill'), mode='lines', name='Awesome Oscillator', line=dict(color='green')),
row=3, col=1
)
fig.add_hline(y=0, line_dash="dash", line_color="gray", row=3, col=1)
if 'MI' in indicators:
fig.add_trace(
go.Scatter(x=data.index, y=indicators['MI'].fillna(method='ffill'), mode='lines', name='Mass Index', line=dict(color='purple')),
row=3, col=1
)
fig.add_hline(y=27, line_dash="dash", line_color="red", row=3, col=1)
if 'IFT_RSI' in indicators:
fig.add_trace(
go.Scatter(x=data.index, y=indicators['IFT_RSI'].fillna(method='ffill'), mode='lines', name='IFT RSI', line=dict(color='orange')),
row=3, col=1
)
# Add momentum and volume indicators to row 4
if 'MACD' in indicators:
macd = indicators['MACD']
macd_line = macd['MACD']
signal_line = macd['SIGNAL']
macd_histogram = macd_line - signal_line
fig.add_trace(
go.Scatter(x=data.index, y=macd_line.fillna(method='ffill'), mode='lines', name='MACD', line=dict(color='#04c6fc')),
row=4, col=1
)
fig.add_trace(
go.Scatter(x=data.index, y=signal_line.fillna(method='ffill'), mode='lines', name='MACD Signal', line=dict(color='blue', dash='dash')),
row=4, col=1
)
fig.add_trace(
go.Bar(x=data.index, y=macd_histogram.fillna(0), name='MACD Histogram', marker_color=['green' if val >= 0 else 'red' for val in macd_histogram]),
row=4, col=1
)
if 'TSI' in indicators:
tsi = indicators['TSI']
fig.add_trace(
go.Scatter(x=data.index, y=tsi['TSI'].fillna(method='ffill'), name='TSI', line=dict(color='blue')),
row=4, col=1
)
fig.add_trace(
go.Scatter(x=data.index, y=tsi['signal'].fillna(method='ffill'), name='TSI Signal', line=dict(color='blue', dash='dash')),
row=4, col=1
)
if 'KST' in indicators:
kst = indicators['KST']
fig.add_trace(
go.Scatter(x=data.index, y=kst['KST'].fillna(method='ffill'), name='KST', line=dict(color='purple')),
row=4, col=1
)
fig.add_trace(
go.Scatter(x=data.index, y=kst['signal'].fillna(method='ffill'), name='KST Signal', line=dict(color='purple', dash='dot')),
row=4, col=1
)
if 'PPO' in indicators:
ppo = indicators['PPO']
fig.add_trace(
go.Scatter(x=data.index, y=ppo['PPO'].fillna(method='ffill'), name='PPO', line=dict(color='cyan')),
row=4, col=1
)
fig.add_trace(
go.Scatter(x=data.index, y=ppo['PPO_signal'].fillna(method='ffill'), name='PPO Signal', line=dict(color='cyan', dash='dash')),
row=4, col=1
)
fig.add_trace(
go.Bar(x=data.index, y=ppo['PPO_histo'].fillna(0), name='PPO Histogram', marker_color=['green' if val >= 0 else 'red' for val in ppo['PPO_histo']]),
row=4, col=1
)
if 'CMO' in indicators:
fig.add_trace(
go.Scatter(x=data.index, y=indicators['CMO'].fillna(method='ffill'), name='CMO', line=dict(color='orange')),
row=4, col=1
)
fig.add_hline(y=50, line_dash="dash", line_color="red", row=4, col=1)
fig.add_hline(y=-50, line_dash="dash", line_color="green", row=4, col=1)
if 'ROC' in indicators:
fig.add_trace(
go.Scatter(x=data.index, y=indicators['ROC'].fillna(method='ffill'), name='ROC', line=dict(color='green')),
row=4, col=1
)
fig.add_hline(y=0, line_dash="dash", line_color="gray", row=4, col=1)
if 'UO' in indicators:
fig.add_trace(
go.Scatter(x=data.index, y=indicators['UO'].fillna(method='ffill'), name='Ultimate Oscillator', line=dict(color='purple')),
row=4, col=1
)
fig.add_hline(y=70, line_dash="dash", line_color="red", row=4, col=1)
fig.add_hline(y=30, line_dash="dash", line_color="green", row=4, col=1)
if 'OBV' in indicators:
fig.add_trace(
go.Scatter(x=data.index, y=indicators['OBV'].fillna(method='ffill'), name='OBV', line=dict(color='blue')),
row=4, col=1
)
if 'ADL' in indicators:
fig.add_trace(
go.Scatter(x=data.index, y=indicators['ADL'].fillna(method='ffill'), name='ADL', line=dict(color='cyan')),
row=4, col=1
)
if 'EFI' in indicators:
fig.add_trace(
go.Scatter(x=data.index, y=indicators['EFI'].fillna(method='ffill'), name='EFI', line=dict(color='magenta')),
row=4, col=1
)
if 'EMV' in indicators:
fig.add_trace(
go.Scatter(x=data.index, y=indicators['EMV'].fillna(method='ffill'), name='EMV', line=dict(color='orange')),
row=4, col=1
)
if 'MFI' in indicators:
fig.add_trace(
go.Scatter(x=data.index, y=indicators['MFI'].fillna(method='ffill'), name='MFI', line=dict(color='blue')),
row=4, col=1
)
fig.add_hline(y=80, line_dash="dash", line_color="red", row=4, col=1)
fig.add_hline(y=20, line_dash="dash", line_color="green", row=4, col=1)
if 'VPT' in indicators:
fig.add_trace(
go.Scatter(x=data.index, y=indicators['VPT'].fillna(method='ffill'), name='VPT', line=dict(color='blue', dash='dot')),
row=4, col=1
)
if 'FVE' in indicators:
fig.add_trace(
go.Scatter(x=data.index, y=indicators['FVE'].fillna(method='ffill'), name='FVE', line=dict(color='blue', dash='dot')),
row=4, col=1
)
if 'VZO' in indicators:
fig.add_trace(
go.Scatter(x=data.index, y=indicators['VZO'].fillna(method='ffill'), name='VZO', line=dict(color='blue', dash='dot')),
row=4, col=1
)
fig.add_hline(y=40, line_dash="dash", line_color="green", row=4, col=1)
fig.add_hline(y=5, line_dash="dash", line_color="red", row=4, col=1)
fig.add_hline(y=-5, line_dash="dash", line_color="red", row=4, col=1)
fig.add_hline(y=-40, line_dash="dash", line_color="green", row=4, col=1)
if 'WTO' in indicators:
wto = indicators['WTO']
fig.add_trace(
go.Scatter(x=data.index, y=wto['WT1'].fillna(method='ffill'), name='WTO WT1', line=dict(color='cyan')),
row=4, col=1
)
fig.add_trace(
go.Scatter(x=data.index, y=wto['WT2'].fillna(method='ffill'), name='WTO WT2', line=dict(color='cyan', dash='dash')),
row=4, col=1
)
if 'Coppock' in indicators:
fig.add_trace(
go.Scatter(x=data.index, y=indicators['Coppock'].fillna(method='ffill'), name='Coppock Curve', line=dict(color='purple')),
row=4, col=1
)
fig.add_hline(y=0, line_dash="dash", line_color="gray", row=4, col=1)
if 'BASP' in indicators:
basp = indicators['BASP']
fig.add_trace(
go.Scatter(x=data.index, y=basp['Buy'].fillna(method='ffill'), name='BASP Buy', line=dict(color='green')),
row=4, col=1
)
fig.add_trace(
go.Scatter(x=data.index, y=basp['Sell'].fillna(method='ffill'), name='BASP Sell', line=dict(color='red')),
row=4, col=1
)
if 'BASPN' in indicators:
baspn = indicators['BASPN']
fig.add_trace(
go.Scatter(x=data.index, y=baspn['BASPN_Buy'].fillna(method='ffill'), name='BASPN Buy', line=dict(color='limegreen')),
row=4, col=1
)
fig.add_trace(
go.Scatter(x=data.index, y=baspn['BASPN_Sell'].fillna(method='ffill'), name='BASPN Sell', line=dict(color='coral')),
row=4, col=1
)
if 'DMI' in indicators:
dmi = indicators['DMI']
fig.add_trace(
go.Scatter(x=data.index, y=dmi['+DI'].fillna(method='ffill'), name='+DI', line=dict(color='blue')),
row=4, col=1
)
fig.add_trace(
go.Scatter(x=data.index, y=dmi['-DI'].fillna(method='ffill'), name='-DI', line=dict(color='red')),
row=4, col=1
)
if 'EBBP' in indicators:
ebbp = indicators['EBBP']
fig.add_trace(
go.Scatter(x=data.index, y=ebbp['Bull'].fillna(method='ffill'), name='Bull Power', line=dict(color='green')),
row=4, col=1
)
fig.add_trace(
go.Scatter(x=data.index, y=ebbp['Bear'].fillna(method='ffill'), name='Bear Power', line=dict(color='red')),
row=4, col=1
)
if 'ATR' in indicators:
fig.add_trace(
go.Scatter(x=data.index, y=indicators['ATR'].fillna(method='ffill'), name='ATR', line=dict(color='blue')),
row=4, col=1
)
# Update layout
fig.update_layout(
title=f'{symbol.upper()} - Technical Analysis',
xaxis_rangeslider_visible=False,
height=800,
showlegend=True
)
st.plotly_chart(fig, use_container_width=True)
# Display indicator values in tabs
st.subheader("📊 Indicator Values")
# Create tabs for different categories
tab1, tab2, tab3, tab4, tab5 = st.tabs(["Trend", "Momentum", "Volume", "Volatility", "Oscillators"])
with tab1:
st.markdown("### Trend Indicators")
trend_cols = st.columns(3)
col_idx = 0
for name, indicator in indicators.items():
if name in ['SMA', 'EMA', 'HMA', 'WMA', 'KAMA', 'FRAMA', 'EVWMA', 'VWAP']:
with trend_cols[col_idx % 3]:
if isinstance(indicator, pd.Series):
st.metric(name, f"{indicator.iloc[-1]:.2f}")
col_idx += 1
with tab2:
st.markdown("### Momentum Indicators")
momentum_cols = st.columns(3)
col_idx = 0
for name, indicator in indicators.items():
if name in ['RSI', 'StochRSI', 'CMO', 'ROC', 'UO']:
with momentum_cols[col_idx % 3]:
if isinstance(indicator, pd.Series):
st.metric(name, f"{indicator.iloc[-1]:.2f}")
col_idx += 1
with tab3:
st.markdown("### Volume Indicators")
volume_cols = st.columns(3)
col_idx = 0
for name, indicator in indicators.items():
if name in ['OBV', 'ADL', 'EFI', 'EMV', 'MFI', 'VPT', 'FVE', 'VZO']:
with volume_cols[col_idx % 3]:
if isinstance(indicator, pd.Series):
st.metric(name, f"{indicator.iloc[-1]:.2f}")
col_idx += 1
with tab4:
st.markdown("### Volatility Indicators")
volatility_cols = st.columns(3)
col_idx = 0
for name, indicator in indicators.items():
if name in ['ATR', 'PSAR']:
with volatility_cols[col_idx % 3]:
if isinstance(indicator, pd.Series):
st.metric(name, f"{indicator.iloc[-1]:.2f}")
col_idx += 1
with tab5:
st.markdown("### Oscillators")
osc_cols = st.columns(3)
col_idx = 0
for name, indicator in indicators.items():
if name in ['ADX', 'CCI', 'Fisher', 'AO', 'MI']:
with osc_cols[col_idx % 3]:
if isinstance(indicator, pd.Series):
st.metric(name, f"{indicator.iloc[-1]:.2f}")
col_idx += 1
# Raw data section
with st.expander("📋 Raw Data"):
st.dataframe(data.tail(50))
# Download section
st.subheader("💾 Download Data")
# Combine all indicators into one DataFrame
combined_df = data.copy()
for name, indicator in indicators.items():
if isinstance(indicator, pd.Series):
combined_df[name] = indicator
elif isinstance(indicator, pd.DataFrame):
for col in indicator.columns:
combined_df[f"{name}_{col}"] = indicator[col]
csv = combined_df.to_csv()
st.download_button(
label="Download CSV",
data=csv,
file_name=f'{symbol}_technical_analysis.csv',
mime='text/csv'
)
except Exception as e:
st.error(f"An error occurred: {str(e)}")
st.error("Please check your internet connection and try again.")
# Instructions
else:
st.markdown("""
## 🚀 How to Use This Dashboard
1. **Enter a stock symbol** in the sidebar (e.g., AAPL, GOOGL, MSFT) for Indian Stocks, use NSE symbols like RELIANCE.NS
or BHEL.NS.
2. **Select time period and interval** for the data
3. **Choose technical indicators** you want to analyze
4. **Adjust parameters** for the indicators
5. **Click "Analyze Stock"** to generate the analysis
### 📈 Available Indicators
This dashboard includes **40+ technical indicators** across multiple categories:
- **Trend Indicators**: SMA, EMA, HMA, WMA, KAMA, FRAMA, EVWMA, VWAP
- **Momentum Indicators**: RSI, MACD, Stochastic RSI, CMO, ROC, TSI, KST, PPO, UO
- **Volume Indicators**: OBV, ADL, Chaikin Oscillator, EFI, EMV, MFI, VPT, FVE, VZO
- **Volatility Indicators**: Bollinger Bands, Keltner Channels, Donchian Channels, ATR, Chandelier Exit, Parabolic SAR
- **Oscillators**: ADX, CCI, Fisher Transform, Awesome Oscillator, Mass Index, Wave Trend Oscillator
- **Complex Indicators**: Ichimoku Cloud, Pivot Points, Fibonacci Pivots, BASP, DMI, Elder Bull/Bear Power
### 💡 Tips
- Use multiple indicators together for better analysis
- Adjust parameters based on your trading timeframe
- Download the data for further analysis
- Check different time periods to understand trends
""")
# Footer
st.markdown("---")
st.markdown("**Technical Analysis Dashboard** | Built with Streamlit & Python | Data from Yahoo Finance")
st.markdown("---")
st.markdown("**Made By Zane Vijay Falcao**")