Update utils/ui_helper.py

utils/ui_helper.py

@@ -5,7 +5,7 @@ import plotly.graph_objects as go
 import pandas as pd
 from utils.helper import *
     
-
+# english
 def main_algo_trader():
     # Front-end Design
     st.set_page_config(layout="wide")
@@ -154,6 +154,164 @@ def main_algo_trader():
 
             st.plotly_chart(fig, use_container_width=True)
 
+            # Download
+            st.download_button(
+                label="Download data as CSV",
+                data=csv,
+                file_name=f'history_{end_date}.csv',
+                mime='text/csv',
+            )
+
+
+# chinese
+def main_algo_trader_chinese():
+    # Front-end Design
+    st.set_page_config(layout="wide")
+    st.write("# 欢迎来到算法交易！一个快速模拟平台👋")
+
+    with st.sidebar:
+        with st.expander("Expand/Collapse"):
+            st.markdown(
+                r"""
+                以下应用程序是成长股策略的简单演示。为简单起见，我们假设我们的研究团队交给了一些股票。在这些股票池中，我们可以做到以下几点：
+                - 使用 `yfinance` 库实时下载股票数据（为了速度，请从时间框架 "1mo" 开始）；
+                - 每个周期（时间框架是一个调整参数），我们通过持有前 n 只股票（n 可以是股票的前五分之一/四分之一）来平衡我们的投资组合（等权）。
+                """
+            )
+
+    # Main inputs
+    tickers = st.text_input(
+        "使用英文键入输入股票代码（以逗号分隔）:",
+        "MSFT, AAPL, NVDA, GOOG, AMZN, META, LLY, AVGO, TSLA, JPM, V, WMT, UNH, MA, PG, HD, JNJ, ORCL, MRK, COST, ABBV, BAC, CRM, AMD, NFLX, ACN, ADBE, DIS, TMO, WFC, MCD, CSCO, ABT, QCOM, INTC, INTU, IBM, AMAT, CMCSA, AXP, PFE, NOW, AMGN, MU",
+    )    
+    start_date = st.sidebar.date_input("开始日期", pd.to_datetime("2001-01-01"))
+    end_date = st.sidebar.date_input("结束日期", pd.to_datetime(datetime.now().strftime("%Y-%m-%d")))
+    time_frame = st.sidebar.selectbox(
+        "选择时间框架:",
+        [
+            "1mo",
+            "3mo",
+        ],
+    )
+    top_n = st.sidebar.number_input("选择前 n 支股票", min_value=1, value=3)
+    height_of_graph = st.sidebar.number_input("图的高度", min_value=500, value=750)
+
+    # Process inputs
+    tickers_list = [ticker.strip() for ticker in tickers.split(",")]
+
+    # Run analysis on button click
+    if st.button("运行分析"):
+        with st.spinner("下载数据并计算回报..."):
+            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 download file
+            @st.cache_data
+            def convert_df(df):
+                # IMPORTANT: Cache the conversion to prevent computation on every rerun
+                return df.to_csv().encode('utf-8')
+            
+            csv = convert_df(df)
+
+            # 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"
+                ),
+                height=height_of_graph,
+            )
+
+            st.plotly_chart(fig, use_container_width=True)
+
             # Download
             st.download_button(
                 label="Download data as CSV",