Finish project 2.

README.md

@@ -36,5 +36,7 @@ unzip -n zips/19fall_optimize_something.zip -d ./
 # Reports
 
 - [Report 1](./martingale/martingale.md)
-- [Report 2](#)
+- [Report 2](./optimize_something/readme.md)
+- [Report 3](#)
+
 

optimize_something/optimization.py

@@ -30,8 +30,42 @@ import pandas as pd
 import matplotlib.pyplot as plt
 import numpy as np
 import datetime as dt
+import math
+import scipy.optimize as opt
 from util import get_data, plot_data
 
+
+def get_sharpe_ratio(daily_returns):
+    SR = daily_returns.mean() / daily_returns.std()
+    k = math.sqrt(252) # daily sampling
+    SR = k * SR
+    return SR
+
+
+def get_daily_returns(df):
+    daily_returns = df.copy()
+    daily_returns[1:] = (df[1:] / df[:-1].values) - 1
+    daily_returns = daily_returns.iloc[1:]
+    return daily_returns
+
+
+def get_daily_portfolio_value(prices, allocs):
+    normed = prices / prices.iloc[0, :]
+    alloced = normed * allocs
+    port_values = alloced.sum(axis=1)
+    return port_values
+
+
+def calculate_stats(prices, allocs):
+    port_value = get_daily_portfolio_value(prices, allocs)
+    cum_ret = (port_value.iloc[-1] / port_value.iloc[0]) - 1
+    daily_returns = get_daily_returns(port_value)
+    avg_daily_ret = daily_returns.mean()
+    std_daily_ret = daily_returns.std()
+    sharpe_ratio = get_sharpe_ratio(daily_returns)
+    return [cum_ret, avg_daily_ret, std_daily_ret, sharpe_ratio]
+
+
 # This is the function that will be tested by the autograder
 # The student must update this code to properly implement the functionality
 def optimize_portfolio(sd=dt.datetime(2008,1,1), ed=dt.datetime(2009,1,1), \
@@ -43,41 +77,52 @@ def optimize_portfolio(sd=dt.datetime(2008,1,1), ed=dt.datetime(2009,1,1), \
     prices = prices_all[syms]  # only portfolio symbols
     prices_SPY = prices_all['SPY']  # only SPY, for comparison later
 
-    plot_data(prices_all)
+    def calc_sr(allocs):
+        port_value = get_daily_portfolio_value(prices, allocs)
+        cum_ret = (port_value.iloc[-1] / port_value.iloc[0]) - 1
+        daily_returns = get_daily_returns(port_value)
+        sharpe_ratio = get_sharpe_ratio(daily_returns)
+        return sharpe_ratio * -1
 
     # find the allocations for the optimal portfolio
-    # note that the values here ARE NOT meant to be correct for a test case
-    allocs = np.asarray([0.2, 0.2, 0.3, 0.3]) # add code here to find the allocations
-    cr, adr, sddr, sr = [0.25, 0.001, 0.0005, 2.1] # add code here to compute stats
-
-    # Get daily portfolio value
-    port_val = prices_SPY # add code here to compute daily portfolio values
+    len_syms = len(syms)
+    allocs = np.asarray([1 / len_syms for _ in range(len_syms)])
+    bounds = [(0, 1) for _ in range(len_syms)]
+    consts = ({'type': 'eq', 'fun': lambda inputs: 1.0 - np.sum(inputs)})
+    result = opt.minimize(calc_sr, x0=allocs, bounds=bounds, constraints=consts)
+    print(result)
+    allocs = result.x
+    cr, adr, sddr, sr = calculate_stats(prices, allocs)
 
     # Compare daily portfolio value with SPY using a normalized plot
     if gen_plot:
-        # add code to plot here
-        df_temp = pd.concat([port_val, prices_SPY], keys=['Portfolio', 'SPY'], axis=1)
-        pass
+        port_val = get_daily_portfolio_value(prices, allocs)
+        prices_SPY_normed = prices_SPY / prices_SPY.iloc[0]
+        df_temp = pd.concat([port_val, prices_SPY_normed], keys=['Portfolio', 'SPY'], axis=1)
+        ax = df_temp.plot(title="SPY and Portfolio Normed", fontsize=12)
+        plt.savefig('plot.png')
+        plt.show()
 
     return allocs, cr, adr, sddr, sr
 
-def test_code():
-    # This function WILL NOT be called by the auto grader
-    # Do not assume that any variables defined here are available to your function/code
-    # It is only here to help you set up and test your code
 
+def report():
+    start_date = dt.datetime(2008,6,1)
+    end_date = dt.datetime(2009,6,1)
+    symbols = ['IBM', 'X', 'GLD', 'JPM']
+    allocations, cr, adr, sddr, sr = optimize_portfolio(sd = start_date, ed = end_date,\
+        syms = symbols, gen_plot = True)
+
+
+def test_code():
     # Define input parameters
     # Note that ALL of these values will be set to different values by
     # the autograder!
-
     start_date = dt.datetime(2009,1,1)
     end_date = dt.datetime(2010,1,1)
     symbols = ['GOOG', 'AAPL', 'GLD', 'XOM', 'IBM']
-
-    # Assess the portfolio
     allocations, cr, adr, sddr, sr = optimize_portfolio(sd = start_date, ed = end_date,\
-        syms = symbols, \
-        gen_plot = False)
+        syms = symbols, gen_plot = True)
 
     # Print statistics
     print(f"Start Date: {start_date}")
@@ -90,6 +135,5 @@ def test_code():
     print(f"Cumulative Return: {cr}")
 
 if __name__ == "__main__":
-    # This code WILL NOT be called by the auto grader
-    # Do not assume that it will be called
-    test_code()
+    # test_code()
+    report()

optimize_something/readme.md

@@ -0,0 +1,7 @@
+# Report
+
+IBM, X, GLD, JPM portfolio optimization for the timeframe from 08-06-01 till
+09-06-01.
+
+![](plot.png)
+