apps updated

app.py

@@ -16,11 +16,7 @@ import streamlit as st
 
 
 def run():
-    st.set_page_config(
-        page_title="Momentum-Strategy",
-        page_icon="💹",
-        layout="wide"
-    )
+    st.set_page_config(page_title="Momentum-Strategy", page_icon="💹", layout="wide")
 
     st.write("# Welcome to the Momentum Strategy Simulator! 💹")
 
@@ -56,6 +52,51 @@ def run():
     """
     )
 
+    with st.expander("Reference (Expand/Collapse)"):
+        st.markdown(
+            f"""
+            # Factor Models and Mutual Fund Evaluation
+
+            ## Monthly Momentum Factor (MOM)
+
+            The Monthly Momentum Factor (MOM) can be calculated by subtracting the equal-weighted average of the lowest performing firms from the equal-weighted average of the highest performing firms, lagged one month (Carhart, 1997). A stock exhibits momentum if its prior 12-month average of returns is positive. Similar to the three-factor model, the momentum factor is defined by a self-financing portfolio of (long positive momentum) + (short negative momentum). Momentum strategies remain popular in financial markets, and financial analysts often incorporate the 52-week price high/low in their Buy/Sell recommendations.
+
+            ## Four-Factor Model
+
+            The four-factor model is commonly used for active management and mutual fund evaluation. Three commonly used methods to adjust a mutual fund's returns for risk are:
+
+            ### 1. Market Model:
+
+            $$
+            EXR_t = α^J + β_mkt * EXMKT_t + ε_t
+            $$
+
+            The intercept in this model is referred to as "Jensen's alpha".
+
+            ### 2. Fama–French Three-Factor Model:
+
+            $$
+            EXR_t = α^FF + β_mkt * EXMKT_t + β_HML * HML_t + β_SMB * SMB_t + ε_t
+            $$
+
+            The intercept in this model is referred to as the "three-factor alpha".
+
+            ### 3. Carhart Four-Factor Model:
+
+            $$
+            EXR_t = α^c + β_mkt * EXMKT_t + β_HML * HML_t + β_SMB * SMB_t + β_UMD * UMD_t + ε_t
+            $$
+
+            The intercept in this model is referred to as the "four-factor alpha".
+
+            `EXR_t` is the monthly return to the asset of concern in excess of the monthly t-bill rate. These models are used to adjust for risk by regressing the excess returns of the asset on an intercept (the alpha) and some factors on the right-hand side of the equation that attempt to control for market-wide risk factors. The right-hand side risk factors include the monthly return of the CRSP value-weighted index less the risk-free rate (`EXMKT_t`), monthly premium of the book-to-market factor (`HML_t`), monthly premium of the size factor (`SMB_t`), and the monthly premium on winners minus losers (`UMD_t`) from Fama-French (1993) and Carhart (1997).
+
+            A fund manager demonstrates forecasting ability when their fund has a positive and statistically significant alpha.
+
+            SMB is a zero-investment portfolio that is long on small capitalization (cap) stocks and short on big-cap stocks. Similarly, HML is a zero-investment portfolio that is long on high book-to-market (B/M) stocks and short on low B/M stocks, and UMD is a zero-cost portfolio that is long previous 12-month return winners and short previous 12-month loser stocks.
+            """
+        )
+
 
 if __name__ == "__main__":
-    run()
+    run()

pages/1_english_.py

@@ -3,4 +3,4 @@ from utils.helper import *
 from utils.ui_helper import *
 
 # run ui
-main_algo_trader()
+main_algo_trader()

pages/2_chinese_.py

@@ -3,4 +3,4 @@ from utils.helper import *
 from utils.ui_helper import *
 
 # run ui
-main_algo_trader_chinese()
+main_algo_trader_chinese()

utils/helper.py

@@ -74,4 +74,4 @@ def create_portfolio_and_calculate_returns(
         new_returns_data["portfolio_returns"] + 1
     ).cumprod()
 
-    return new_returns_data
+    return new_returns_data

utils/ui_helper.py

@@ -4,7 +4,8 @@ import streamlit as st
 import plotly.graph_objects as go
 import pandas as pd
 from utils.helper import *
-    
+
+
 # english
 def main_algo_trader():
     # Front-end Design
@@ -25,9 +26,11 @@ def main_algo_trader():
     tickers = st.text_input(
         "Enter tickers (comma-separated):",
         "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("Start date", pd.to_datetime("2001-01-01"))
-    end_date = st.sidebar.date_input("End date", pd.to_datetime(datetime.now().strftime("%Y-%m-%d")))
+    end_date = st.sidebar.date_input(
+        "End date", pd.to_datetime(datetime.now().strftime("%Y-%m-%d"))
+    )
     time_frame = st.sidebar.selectbox(
         "Select Time Frame:",
         [
@@ -36,7 +39,9 @@ def main_algo_trader():
         ],
     )
     top_n = st.sidebar.number_input("Top n stocks", min_value=1, value=3)
-    height_of_graph = st.sidebar.number_input("Height of the plot", min_value=500, value=750)
+    height_of_graph = st.sidebar.number_input(
+        "Height of the plot", min_value=500, value=750
+    )
 
     # Process inputs
     tickers_list = [ticker.strip() for ticker in tickers.split(",")]
@@ -69,8 +74,8 @@ def main_algo_trader():
             @st.cache_data
             def convert_df(df):
                 # IMPORTANT: Cache the conversion to prevent computation on every rerun
-                return df.to_csv().encode('utf-8')
-            
+                return df.to_csv().encode("utf-8")
+
             csv = convert_df(df)
 
             # Create plot
@@ -118,14 +123,18 @@ def main_algo_trader():
             portfolio_std = returns_data["portfolio_returns"].std()
 
             # Update title text with additional information
-            if time_frame == '1mo':
+            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)
+                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)
+                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}, "
@@ -158,8 +167,8 @@ def main_algo_trader():
             st.download_button(
                 label="Download data as CSV",
                 data=csv,
-                file_name=f'history_{end_date}.csv',
-                mime='text/csv',
+                file_name=f"history_{end_date}.csv",
+                mime="text/csv",
             )
 
 
@@ -183,9 +192,11 @@ def main_algo_trader_chinese():
     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")))
+    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:",
         [
@@ -227,8 +238,8 @@ def main_algo_trader_chinese():
             @st.cache_data
             def convert_df(df):
                 # IMPORTANT: Cache the conversion to prevent computation on every rerun
-                return df.to_csv().encode('utf-8')
-            
+                return df.to_csv().encode("utf-8")
+
             csv = convert_df(df)
 
             # Create plot
@@ -276,23 +287,27 @@ def main_algo_trader_chinese():
             portfolio_std = returns_data["portfolio_returns"].std()
 
             # Update title text with additional information
-            if time_frame == '1mo':
+            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)
+                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)
+                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"业绩:<br>"
+                f"标杆回报风险比 = {benchmark_sharpe_ratio:.3f}, "
+                f"投资组合回报风险比 = {portfolio_sharpe_ratio:.3f}, "
+                f"交易窗口: {time_frame}<br>"
+                f"标杆 => Mean: {benchmark_mean:.4f}, Std: {benchmark_std:.4f}; "
+                f"投资组合 => 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"这个数字如何理解，我们以1000块钱计算以下: <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}."
             )
@@ -316,6 +331,6 @@ def main_algo_trader_chinese():
             st.download_button(
                 label="Download data as CSV",
                 data=csv,
-                file_name=f'history_{end_date}.csv',
-                mime='text/csv',
-            )
+                file_name=f"history_{end_date}.csv",
+                mime="text/csv",
+            )