Create ui_helper.py

utils/ui_helper.py

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+# app.py
+import streamlit as st
+import plotly.graph_objects as go
+import pandas as pd
+from helpers.app_helper_algotrader import *
+
+
+def main_algo_trader():
+    st.write("# Welcome to Algorithmic Trading! A Toy Implementation👋")
+    st.sidebar.success("Select a session from the menu of content.")
+
+    st.markdown(
+        r"""
+        The following app is a simple demonstration of the growth stock strategy. For simplicity, we assume our research team hand over a pool of stocks. Amongst the pool of stocks, we can do the following:
+        - use `yfinance` library to download stock data live (for the sake of speed, please start with time frame "1mo");
+        - every period (time frame is a tuning parameter), we balance our portfolio (equal weight) by holding the top n stocks (n can be top quintile/quartile of stocks);
+        """
+    )
+
+    # Main inputs
+    tickers = st.text_input(
+        "Enter tickers (comma-separated):",
+        "AXP, AMGN, AAPL, BA, CAT, CSCO, CVX, GS, HD, HON, IBM, INTC, JNJ, KO, JPM, MCD, MMM, MRK, MSFT, NKE, PG, TRV, UNH, CRM, VZ, V, WBA, WMT, DIS, DOW, XOM, WFC, MA, COST, AVGO, ADBE, C, NFLX, PYPL, TSLA, NVDA",
+    )
+    start_date = st.date_input("Start date", pd.to_datetime("2001-01-01"))
+    end_date = st.date_input("End date", pd.to_datetime("2023-12-31"))
+    time_frame = st.selectbox(
+        "Select Time Frame:",
+        [
+            "1mo",
+            "3mo",
+        ],
+    )
+    top_n = st.number_input("Top n stocks", min_value=1, value=3)
+
+    # Process inputs
+    tickers_list = [ticker.strip() for ticker in tickers.split(",")]
+
+    # Run analysis on button click
+    if st.button("Run Analysis"):
+        with st.spinner("Downloading data and calculating returns..."):
+            stock_data = download_stock_data(
+                tickers_list,
+                start_date.strftime("%Y-%m-%d"),
+                end_date.strftime("%Y-%m-%d"),
+                w=time_frame,
+            )
+            returns_data = create_portfolio_and_calculate_returns(stock_data, top_n)
+            benchmark_sharpe_ratio = (
+                returns_data["benchmark"].mean() / returns_data["benchmark"].std()
+            )
+            portfolio_sharpe_ratio = (
+                returns_data["portfolio_returns"].mean()
+                / returns_data["portfolio_returns"].std()
+            )
+
+            # Data for plotting
+            df = returns_data[
+                ["rolling_benchmark", "rolling_portfolio_returns", "portfolio_history"]
+            ]
+            df.index = pd.to_datetime(df.index, unit="ms")
+
+            # Create plot
+            fig = go.Figure()
+            fig.add_trace(
+                go.Scatter(
+                    x=df.index,
+                    y=df["rolling_benchmark"],
+                    mode="lines",
+                    name="Rolling Benchmark",
+                )
+            )
+            fig.add_trace(
+                go.Scatter(
+                    x=df.index,
+                    y=df["rolling_portfolio_returns"],
+                    mode="lines",
+                    name="Rolling Portfolio Returns",
+                )
+            )
+
+            for date, stocks in df["portfolio_history"].items():
+                fig.add_shape(
+                    type="line",
+                    x0=date,
+                    y0=0,
+                    x1=date,
+                    y1=0,
+                    line=dict(color="RoyalBlue", width=1),
+                )
+                fig.add_annotation(
+                    x=date,
+                    y=0.5,
+                    text=str(stocks),
+                    showarrow=False,
+                    yshift=10,
+                    textangle=-90,
+                    font=dict(size=15),  # You can adjust the size as needed
+                )
+
+            # Calculate means and standard deviations
+            benchmark_mean = returns_data["benchmark"].mean()
+            benchmark_std = returns_data["benchmark"].std()
+            portfolio_mean = returns_data["portfolio_returns"].mean()
+            portfolio_std = returns_data["portfolio_returns"].std()
+
+            # Update title text with additional information
+            if time_frame == '1mo':
+                some_n_based_on_time_frame = 12
+                in_a_year = 1000*(1+portfolio_mean)**(some_n_based_on_time_frame)
+                in_50_years = 1000*(1+portfolio_mean)**(some_n_based_on_time_frame*50)
+            else:
+                some_n_based_on_time_frame = 4
+                in_a_year = 1000*(1+portfolio_mean)**(some_n_based_on_time_frame)
+                in_50_years = 1000*(1+portfolio_mean)**(some_n_based_on_time_frame*50)
+            title_text = (
+                f"Performance:<br>"
+                f"Benchmark Sharpe Ratio = {benchmark_sharpe_ratio:.3f}, "
+                f"Portfolio Sharpe Ratio = {portfolio_sharpe_ratio:.3f}, "
+                f"based on time frame: {time_frame}<br>"
+                f"Benchmark => Mean: {benchmark_mean:.4f}, Std: {benchmark_std:.4f}; "
+                f"Portfolio => Mean: {portfolio_mean:.4f}, Std: {portfolio_std:.4f}<br>"
+                f"---<br>"
+                f"This may or may not be a small number, let's check: <br>"
+                f"$1,000*(1+{portfolio_mean:.4f})^({some_n_based_on_time_frame})={in_a_year}, <br>"
+                f"$1,000*(1+{portfolio_mean:.4f})^({some_n_based_on_time_frame}*50)={in_50_years}."
+            )
+            curr_max_num = max(
+                df.rolling_benchmark.max(), df.rolling_portfolio_returns.max()
+            )
+            fig.update_layout(
+                title=title_text,
+                xaxis_title="Date",
+                yaxis_title="Value",
+                yaxis=dict(range=[0, curr_max_num * 1.1]),
+                legend=dict(
+                    orientation="h", x=0.5, y=-0.4, xanchor="center", yanchor="bottom"
+                ),
+            )
+
+            st.plotly_chart(fig, use_container_width=True)