Create app.py

app.py

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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.")
+short_window = st.sidebar.slider(
+    "Select short window size", min_value=2, max_value=200, value=12
+)
+long_window = st.sidebar.slider(
+    "Select long window size", min_value=2, max_value=250, value=50
+)
+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")
+)
+
+# 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.")