upload shap_ratio.py

sharp_ratio.py

@@ -0,0 +1,150 @@
+import pandas as pd
+import numpy as np
+from datetime import datetime
+import streamlit as st
+import matplotlib.pyplot as plt
+import plotly.express as px
+#import plotly.graph_objects as go
+
+def preprocess(stocks,choices):
+    symbols, weights, investment = choices.values()
+    stocks = stocks.pivot(index="Date", columns="Ticker", values="Adj. Close")
+    print('stocks',stocks)
+    logRet = np.log(stocks/stocks.shift())
+    log_returns = np.log(stocks/stocks.shift())
+    tickers_list = symbols.copy()
+    weights_list = weights.copy()
+    return logRet,tickers_list,weights_list
+
+def cumulative_return(stocks,choices):
+    symbols, weights, investment = choices.values()
+    logRet,tickers_list,weights_list = preprocess(stocks,choices)
+    tkers = sorted(set(stocks['Ticker'].unique()))
+
+    stocks = stocks.pivot(index="Date", columns="Ticker", values="Adj. Close")
+    
+    stock_port = {}
+    for e in tickers_list: stock_port[e] = 0
+    # Convert Weights to Floats and Sum
+    weights = [float(x) for x in weights_list]
+    s = sum(weights)
+    # Calc Weight Proportions
+    new_weights = []
+    for i in weights: new_weights.append(i/s)
+    # Assign Weights to Ticker Dict
+    i = 0
+    for e in stock_port:
+        stock_port[e] = new_weights[i]
+        i += 1
+    
+    port = dict.fromkeys(tkers, 0)
+    port.update(stock_port)
+    
+    portfolio_dict = port
+    
+    for e in portfolio_dict:
+        tmp = 0
+        if portfolio_dict[e] > tmp:
+            tmp = portfolio_dict[e]
+            tick = e
+    list_ =[]        
+    for e in tickers_list:
+        if e not in list_:
+            list_.append(e)
+
+    df = stocks[list_]
+    df = df/df.iloc[0]
+    df.reset_index(inplace=True)
+    df=pd.DataFrame(df)
+    print(df)
+    fig = px.line(df, x='Date' ,y=df.columns[1:,])
+    
+    
+    #layout reference = https://linuxtut.com/en/b13e3e721519c2842cc9/
+    fig.update_layout(
+        xaxis=dict(
+        rangeselector=dict(
+                buttons=list([
+                    dict(count=1,
+                         label="1m",
+                         step="month",
+                         stepmode="backward"),
+                    dict(count=6,
+                         label="6m",
+                         step="month",
+                         stepmode="backward"),
+                    dict(count=1,
+                         label="YTD",
+                         step="year",
+                         stepmode="todate"),
+                    dict(count=1,
+                         label="1y",
+                         step="year",
+                         stepmode="backward"),
+                    dict(step="all")
+                ])
+            ),
+            rangeslider=dict(
+                visible=True
+            ),
+            type="date"
+        )
+    )
+    fig.update_layout(xaxis=dict(rangeselector = dict(font = dict( color = "black"))))
+    st.subheader('Portfolio Historical Normalized Cumulative Returns')
+
+    st.plotly_chart(fig, use_container_width=True)
+    
+def sharp_ratio_func(stocks,choices):
+    symbols, weights, investment = choices.values()
+    logRet,tickers_list,weights_list = preprocess(stocks,choices)
+    tkers = sorted(set(stocks['Ticker'].unique()))
+
+    stocks = stocks.pivot(index="Date", columns="Ticker", values="Adj. Close")
+        
+    stock_port = {}
+    for e in tickers_list: stock_port[e] = 0
+    # Convert Weights to Floats and Sum    
+    weights = [float(x) for x in weights_list]
+    s = sum(weights)
+    # Calc Weight Proportions
+    new_weights = []
+    for i in weights: new_weights.append(i/s)
+    # Assign Weights to Ticker Dict
+    i = 0
+    for e in stock_port:
+        stock_port[e] = new_weights[i]
+        i += 1
+        
+    port = dict.fromkeys(tkers, 0)
+    port.update(stock_port)
+        
+    portfolio_dict = port
+            
+    sharp_ratio_list = []
+    for ticker in symbols:
+        logRet = np.log(stocks/stocks.shift())
+        stk = dict.fromkeys(tkers, 0)
+        stkTicker = {ticker:1}
+        stk.update(stkTicker)
+        ttlStk = np.sum(logRet*stk, axis=1)
+        stock_sharpe_ratio = ttlStk.mean() / ttlStk.std()
+        sharp_ratio_list.append(stock_sharpe_ratio)
+            
+    sharp_ratio = {'Assets': symbols, 'Sharpe Ratio': sharp_ratio_list}
+        
+        # Portfolio sharp Ratio Calculation
+    logRet = np.log(stocks/stocks.shift())
+    portfolio = dict.fromkeys(tkers, 0)
+    portfolio.update(portfolio_dict)
+    totalPortfolio = np.sum(logRet*portfolio, axis=1)
+    portfolio_sharpe_ratio = totalPortfolio.mean() / totalPortfolio.std()
+        
+    sharp_ratio['Assets'].append('Portfolio')
+    sharp_ratio['Sharpe Ratio'].append(portfolio_sharpe_ratio)
+    
+    fig = px.bar(sharp_ratio, x='Assets', y="Sharpe Ratio",color='Assets') 
+    fig.update_layout(title_text = 'Sharpe Ratio of the Assets and Portfolio',
+                        title_x=0.458)
+    st.plotly_chart(fig, use_container_width=True)
+