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import streamlit as st
from thronetrader import StrategicSignals
from thronetrader.helper.squire import classify # Import your classification method
import pandas as pd
def main():
st.title("Strategic Trading Signals")
# 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(symbol)
# 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 strategic trading signals
strategic_signals = StrategicSignals(df)
st.subheader("Bollinger Bands Signals:")
bollinger_bands_signals = strategic_signals.get_bollinger_bands_signals()
display_signals(bollinger_bands_signals)
st.subheader("Breakout Signals:")
breakout_signals = strategic_signals.get_breakout_signals()
display_signals(breakout_signals)
st.subheader("Crossover Signals:")
crossover_signals = strategic_signals.get_crossover_signals()
display_signals(crossover_signals)
st.subheader("MACD Signals:")
macd_signals = strategic_signals.get_macd_signals()
display_signals(macd_signals)
st.subheader("RSI Signals:")
rsi_signals = strategic_signals.get_rsi_signals()
display_signals(rsi_signals)
# Example of using your classify method
classification_result = classify(df, logger) # Pass your DataFrame and logger
st.subheader("Classification Result:")
st.write(classification_result)
def fetch_alpha_vantage_data(symbol):
api_key = "QR8F9B7T6R2SWTAT"
url = f'https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol={symbol}&interval=5min&apikey={api_key}'
response = requests.get(url)
alpha_vantage_data = response.json()
return alpha_vantage_data
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(signals):
for signal in signals:
if isinstance(signal, dict):
st.write(f"Date: {signal.get('date', 'N/A')}")
st.write(f"Signal: {signal.get('signal', 'N/A')}")
else:
st.write("Invalid signal format.")
if __name__ == "__main__":
main() |