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import pandas as pd
import plotly.graph_objects as go
import pygwalker as pyg
import streamlit as st
import streamlit.components.v1 as components
import yfinance as yf
from plotly.subplots import make_subplots
from scipy.stats import norm
from utils.helper import *
# Streamlit app layout
st.set_page_config(layout="wide")
st.title("📊 Technical Trading Strategy Simulation 💹")
# Sidebar inputs
ticker = st.sidebar.text_input("Enter Stock Ticker", "AAPL").upper()
start_date = st.sidebar.date_input("Start Date", pd.to_datetime("2020-01-01"))
end_date = st.sidebar.date_input("End Date", pd.to_datetime("today"))
# Add sidebar slider for selecting two integers
st.sidebar.success("Please select your own parameters.")
# Expander
with st.expander("Expand to fine tune the windows:"):
short_window = st.sidebar.slider(
"Select short window size", min_value=2, max_value=200, value=20
)
long_window = st.sidebar.slider(
"Select long window size", min_value=2, max_value=250, value=95
)
signal_window = st.sidebar.slider(
"Select signal window size", min_value=2, max_value=250, value=9
)
values = st.sidebar.slider(
"Select a range of values", min_value=-100, max_value=100, value=(-10, 10), step=1
)
option = st.sidebar.selectbox(
"How would you like rescale data?", ("Original", "Normalization", "Percentile")
)
st.sidebar.markdown(
f"""
## URL of the app: [here](https://huggingface.co/spaces/eagle0504/technical-trader).
"""
)
# Add submit button in the sidebar
submit_button = st.sidebar.button("Submit")
# Update to execute changes only when the submit button is clicked
if submit_button:
with st.spinner("Wait for it..."):
# Message
if option == "Original":
st.success(
"We use the stock price (within the range selected) to create the MACD and Signal Line (which numerically vary based on price data)."
)
elif option == "Normalization":
st.success(
"We use the stock price (within the range selected) to create the MACD and Signal Line (which numerically vary based on price data). Next, we normalize the MACD/Signal Line so that fall in a consistent range, i.e. approximately from -2 to 2."
)
else:
st.success(
"We use the stock price (within the range selected) to create the MACD and Signal Line (which numerically vary based on price data). Next, we normalize the MACD/Signal Line so that fall in a consistent range, i.e. approximately from -2 to 2. Last, we use the normalized data to create probabilities from -100% to +100%. The probability means statistically what is believed to reverse the current direction."
)
# Download stock data
data = yf.download(ticker, start=start_date, end=end_date)
if not data.empty:
if option == "Normalization":
data = calculate_normalized_macd(
data, short_window, long_window, signal_window
)
some_warning_message = "normalized data"
elif option == "Percentile":
data = calculate_percentile_macd(
data, short_window, long_window, signal_window
)
some_warning_message = "percentile data (numbers in %)"
else:
data = calculate_macd(data, short_window, long_window, signal_window)
some_warning_message = "original data"
data = find_crossovers(data, values[0], values[1])
# Plotting
fig = create_fig(data, ticker)
st.plotly_chart(fig, use_container_width=True)
st.warning(f"In the above graph, we use {some_warning_message}.")
else:
st.write("No data available for the given ticker.")
# New section to get and display fundamentals data under an expander
with st.expander("View Fundamentals Data"):
a, b, c = get_fundamentals(ticker)
fundamentals_data = pd.concat([a, b, c], axis=0)
fundamentals_data_t = fundamentals_data.transpose()
fundamentals_data_t["date"] = fundamentals_data_t.index
fundamentals_data_t.reset_index(drop=True, inplace=True)
if not fundamentals_data.empty:
# Generate a table (crude way)
# st.table(fundamentals_data)
# Generate the HTML using Pygwalker (user-friendly and flexible)
pyg_html = pyg.walk(fundamentals_data_t, return_html=True)
# Embed the HTML into the Streamlit app
components.html(pyg_html, height=1000, scrolling=True)
else:
st.write("No fundamentals data available for the given ticker.")
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