Start working on strategy evaluation

marketsim/marketsim.py

@@ -75,6 +75,7 @@ def get_portfolio_value(holding, prices):
 def handle_order(date, order, holding, prices, commission, impact):
     """Process the order."""
     symbol, order, shares = order
+    assert(shares > 0) # Can only buy or sell positive amount of shares.
     adj_closing_price = prices[symbol]
     cost = shares * adj_closing_price
     # Charge commission and deduct impact penalty

strategy_evaluation/BenchmarkStrategy.py

@@ -0,0 +1,36 @@
+import pandas as pd
+import util as ut
+import datetime as dt
+
+
+class BenchmarkStrategy:
+    def __init__(self, verbose=False, impact=0.0, commission=0.0):
+        self.verbose = verbose
+        self.impact = impact
+        self.commission = commission
+
+    def addEvidence(self, symbol=0, sd=0, ed=0, sv=0):
+        """Keep this so that API is valid."""
+        pass
+
+    def testPolicy(self, symbol="IBM",
+                   sd=dt.datetime(2009, 1, 1),
+                   ed=dt.datetime(2010, 1, 1),
+                   sv=10000):
+        """Benchmark is to buy 1000 shares and hold."""
+        dates = pd.date_range(sd, ed)
+        prices = ut.get_data([symbol], dates)  # automatically adds SPY
+
+        orders = pd.DataFrame(index=prices.index)
+        orders["Symbol"] = symbol
+        orders["Order"] = ""
+        orders["Shares"] = 0
+
+        orders.iloc[0] = [symbol, "BUY", 1000]
+        orders.iloc[-1] = [symbol, "SELL", 1000]
+        orders = orders[orders["Shares"] != 0]
+
+        if self.verbose:
+            print(type(orders))  # it better be a DataFrame!
+            print(orders)
+        return orders

strategy_evaluation/ManualStrategy.py

@@ -0,0 +1,58 @@
+import datetime as dt
+import pandas as pd
+import util as ut
+
+
+class ManualStrategy:
+    def __init__(self, verbose=False, impact=0.0, commission=0.0):
+        self.verbose = verbose
+        self.impact = impact
+        self.commission = commission
+
+    # this method should create a QLearner, and train it for trading
+    def addEvidence(self, symbol="IBM",
+                    sd=dt.datetime(2008, 1, 1),
+                    ed=dt.datetime(2009, 1, 1),
+                    sv=10000):
+
+        # add your code to do learning here
+
+        # example usage of the old backward compatible util function
+        syms = [symbol]
+        dates = pd.date_range(sd, ed)
+        prices_all = ut.get_data(syms, dates)  # automatically adds SPY
+        prices = prices_all[syms]  # only portfolio symbols
+        # prices_SPY = prices_all['SPY']  # only SPY, for comparison later
+        if self.verbose:
+            print(prices)
+
+        # example use with new colname
+        # automatically adds SPY
+        volume_all = ut.get_data(syms, dates, colname="Volume")
+        volume = volume_all[syms]  # only portfolio symbols
+        # volume_SPY = volume_all['SPY']  # only SPY, for comparison later
+        if self.verbose:
+            print(volume)
+
+    # this method should use the existing policy and test it against new data
+    def testPolicy(self, symbol="IBM",
+                   sd=dt.datetime(2009, 1, 1),
+                   ed=dt.datetime(2010, 1, 1),
+                   sv=10000):
+        dates = pd.date_range(sd, ed)
+        prices = ut.get_data([symbol], dates)  # automatically adds SPY
+        orders = pd.DataFrame(index=prices.index)
+        orders["Symbol"] = symbol
+        orders["Order"] = ""
+        orders["Shares"] = 0
+
+        # here we build a fake set of trades
+        orders.iloc[0] = [symbol, "BUY", 1000]
+        orders.iloc[40] = [symbol, "SELL", 1000]
+        orders.iloc[41] = [symbol, "BUY", 1000]
+        orders.iloc[60] = [symbol, "SELL", 2000]
+        orders.iloc[61] = [symbol, "BUY", 2000]
+        orders.iloc[-1] = [symbol, "SELL", 1000]
+        orders = orders[orders["Shares"] != 0]
+        return orders
+

strategy_evaluation/StrategyLearner.py

@@ -1,89 +1,88 @@
-"""  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+"""
-Template for implementing StrategyLearner  (c) 2016 Tucker Balch  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+Template for implementing StrategyLearner  (c) 2016 Tucker Balch
-  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+
-Copyright 2018, Georgia Institute of Technology (Georgia Tech)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+Copyright 2018, Georgia Institute of Technology (Georgia Tech)
-Atlanta, Georgia 30332  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+Atlanta, Georgia 30332
-All Rights Reserved  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+All Rights Reserved
-  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+
-Template code for CS 4646/7646  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+Template code for CS 4646/7646
-  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+
-Georgia Tech asserts copyright ownership of this template and all derivative  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+Georgia Tech asserts copyright ownership of this template and all derivative
-works, including solutions to the projects assigned in this course. Students  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+works, including solutions to the projects assigned in this course. Students
-and other users of this template code are advised not to share it with others  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+and other users of this template code are advised not to share it with others
-or to make it available on publicly viewable websites including repositories  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+or to make it available on publicly viewable websites including repositories
-such as github and gitlab.  This copyright statement should not be removed  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+such as github and gitlab.  This copyright statement should not be removed
-or edited.  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+or edited.
-  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+
-We do grant permission to share solutions privately with non-students such  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+We do grant permission to share solutions privately with non-students such
-as potential employers. However, sharing with other current or future  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+as potential employers. However, sharing with other current or future
-students of CS 7646 is prohibited and subject to being investigated as a  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+students of CS 7646 is prohibited and subject to being investigated as a
-GT honor code violation.  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+GT honor code violation.
-  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+
------do not edit anything above this line---  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+-----do not edit anything above this line---
-  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+
-Student Name: Tucker Balch (replace with your name)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+Student Name: Tucker Balch (replace with your name)
-GT User ID: tb34 (replace with your User ID)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+GT User ID: tb34 (replace with your User ID)
-GT ID: 900897987 (replace with your GT ID)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+GT ID: 900897987 (replace with your GT ID)
-"""  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+"""
-  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+
-import datetime as dt  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+import datetime as dt
-import pandas as pd  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+import pandas as pd
-import util as ut  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+import util as ut
-import random  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+
-  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+class StrategyLearner(object):
-class StrategyLearner(object):  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+
-  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    # constructor
-    # constructor  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    def __init__(self, verbose = False, impact=0.0, commission=0.0):
-    def __init__(self, verbose = False, impact=0.0, commission=0.0):  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        self.verbose = verbose
-        self.verbose = verbose  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        self.impact = impact
-        self.impact = impact  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        self.commission = commission
-        self.commission = commission  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+
-  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    # this method should create a QLearner, and train it for trading
-    # this method should create a QLearner, and train it for trading  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
     def addEvidence(self, symbol = "IBM", \
         sd=dt.datetime(2008,1,1), \
         ed=dt.datetime(2009,1,1), \
-        sv = 10000):  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        sv = 10000):
-  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+
-        # add your code to do learning here  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        # add your code to do learning here
-  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+
-        # example usage of the old backward compatible util function  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        # example usage of the old backward compatible util function
-        syms=[symbol]  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        syms=[symbol]
-        dates = pd.date_range(sd, ed)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        dates = pd.date_range(sd, ed)
-        prices_all = ut.get_data(syms, dates)  # automatically adds SPY  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        prices_all = ut.get_data(syms, dates)  # automatically adds SPY
-        prices = prices_all[syms]  # only portfolio symbols  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        prices = prices_all[syms]  # only portfolio symbols
-        prices_SPY = prices_all['SPY']  # only SPY, for comparison later  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        # prices_SPY = prices_all['SPY']  # only SPY, for comparison later
-        if self.verbose: print(prices)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        if self.verbose: print(prices)
-  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+
-        # example use with new colname  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        # example use with new colname
-        volume_all = ut.get_data(syms, dates, colname = "Volume")  # automatically adds SPY  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        volume_all = ut.get_data(syms, dates, colname = "Volume")  # automatically adds SPY
-        volume = volume_all[syms]  # only portfolio symbols  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        volume = volume_all[syms]  # only portfolio symbols
-        volume_SPY = volume_all['SPY']  # only SPY, for comparison later  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        # volume_SPY = volume_all['SPY']  # only SPY, for comparison later
-        if self.verbose: print(volume)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        if self.verbose: print(volume)
-  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+
-    # this method should use the existing policy and test it against new data  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    # this method should use the existing policy and test it against new data
     def testPolicy(self, symbol = "IBM", \
         sd=dt.datetime(2009,1,1), \
         ed=dt.datetime(2010,1,1), \
-        sv = 10000):  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        sv = 10000):
-  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+
-        # here we build a fake set of trades  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        # here we build a fake set of trades
-        # your code should return the same sort of data  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        # your code should return the same sort of data
-        dates = pd.date_range(sd, ed)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        dates = pd.date_range(sd, ed)
-        prices_all = ut.get_data([symbol], dates)  # automatically adds SPY  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        prices_all = ut.get_data([symbol], dates)  # automatically adds SPY
-        trades = prices_all[[symbol,]]  # only portfolio symbols  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        trades = prices_all[[symbol,]]  # only portfolio symbols
-        trades_SPY = prices_all['SPY']  # only SPY, for comparison later  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        # trades_SPY = prices_all['SPY']  # only SPY, for comparison later
-        trades.values[:,:] = 0 # set them all to nothing  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        trades.values[:,:] = 0 # set them all to nothing
-        trades.values[0,:] = 1000 # add a BUY at the start  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        trades.values[0,:] = 1000 # add a BUY at the start
-        trades.values[40,:] = -1000 # add a SELL  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        trades.values[40,:] = -1000 # add a SELL
-        trades.values[41,:] = 1000 # add a BUY  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        trades.values[41,:] = 1000 # add a BUY
-        trades.values[60,:] = -2000 # go short from long  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        trades.values[60,:] = -2000 # go short from long
-        trades.values[61,:] = 2000 # go long from short  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        trades.values[61,:] = 2000 # go long from short
-        trades.values[-1,:] = -1000 #exit on the last day  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        trades.values[-1,:] = -1000 #exit on the last day
-        if self.verbose: print(type(trades)) # it better be a DataFrame!  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        if self.verbose: print(type(trades)) # it better be a DataFrame!
-        if self.verbose: print(trades)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        if self.verbose: print(trades)
-        if self.verbose: print(prices_all)  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        if self.verbose: print(prices_all)
-        return trades  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+        return trades
-  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+
-if __name__=="__main__":  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+if __name__=="__main__":
-    print("One does not simply think up a strategy")  		  	   		     			  		 			     			  	  		 	  	 		 			  		  			
+    print("One does not simply think up a strategy")

strategy_evaluation/experiment1.py

@@ -0,0 +1,141 @@
+import pandas as pd
+import datetime as dt
+import marketsim.marketsim as marketsim
+import indicators
+import util
+import matplotlib.pyplot as plt
+from matplotlib.widgets import MultiCursor
+from BenchmarkStrategy import BenchmarkStrategy
+from ManualStrategy import ManualStrategy
+
+
+def macd_strat(macd):
+    def strat(x):
+        print(x)
+
+    macd['macd_trigger'] = macd.rolling(2).apply(strat)
+
+    # for i, row in macd.iterrows():
+        # if i == 0:
+            # continue
+        # print(row)
+        # prev_macd, prev_signal, _ = row
+        # cur_macd, cur_signal, _ = row
+        # if cur_macd < -.5 and (prev_macd < prev_signal) \
+                          # and (cur_macd > cur_signal):
+            # macd.iloc[i]['macd_buy_sell'] = 1
+
+def experiment1():
+    symbol = "JPM"
+    start_value = 10000
+    sd = dt.datetime(2008, 1, 1)
+    ed = dt.datetime(2009, 12, 31)
+    df = util.get_data([symbol], pd.date_range(sd, ed))
+    df.drop(columns=["SPY"], inplace=True)
+    # df = pd.DataFrame(index=df.index)
+
+    bs = BenchmarkStrategy()
+    orders = bs.testPolicy(symbol, sd, ed, start_value)
+    df["Benchmark"] = marketsim.compute_portvals(orders, start_value)
+
+    ms = ManualStrategy()
+    orders = ms.testPolicy(symbol, sd, ed, start_value)
+    df["Manual"] = marketsim.compute_portvals(orders, start_value)
+
+    # indicators.price_sma(df, symbol, 21)
+    # sma = indicators.sma(df, symbol, [9, 21])
+    # rsi = indicators.rsi(df, symbol)
+    macd = indicators.macd(df, symbol).copy()
+    # macd_strat(macd)
+
+    fig, ax = plt.subplots(2, sharex=True)
+    df[symbol].plot(ax=ax[0])
+    # sma.plot(ax=ax[0])
+    macd.plot(ax=ax[1])
+    # macd.iloc[:,0].plot(ax=ax[1])
+    # rsi.plot(ax=ax[2])
+    # df[["Benchmark", "Manual"]].plot(ax=ax[3])
+
+    # XXX: Plot where we buy and sell.
+
+    for a in ax: a.grid()
+    multi = MultiCursor(fig.canvas, ax, color='r', lw=0.5)
+    plt.show()
+
+    # df.plot(title="results", subplots=True)
+    #sd = dt.datetime(2008, 1, 1)
+    #ed = dt.datetime(2009, 12, 31)
+    #df = get_data([symbol], pd.date_range(sd, ed))
+    #df.drop(columns=["SPY"], inplace=True)
+    # df_orig = df.copy()
+    #df = indicators.normalize(df)
+    #indicators.price_sma(df, symbol, 21)
+    #df.plot(title="21 SMA and EMA")
+    #plt.show()
+
+    # You may use data from other symbols (such as SPY) to inform both your
+    # Manual Learner and Strategy Learner. The in-sample/development period is
+    # January 1, 2008 to December 31 2009. The out-of-sample/testing period is
+    # January 1, 2010 to December 31 2011.
+
+
+class BlittedCursor:
+    """
+    A cross hair cursor using blitting for faster redraw.
+    """
+    def __init__(self, ax):
+        self.ax = ax
+        self.background = None
+        self.horizontal_line = ax.axhline(color='k', lw=0.8, ls='--')
+        self.vertical_line = ax.axvline(color='k', lw=0.8, ls='--')
+        # text location in axes coordinates
+        self.text = ax.text(0.72, 0.9, '', transform=ax.transAxes)
+        self._creating_background = False
+        ax.figure.canvas.mpl_connect('draw_event', self.on_draw)
+
+    def on_draw(self, event):
+        self.create_new_background()
+
+    def set_cross_hair_visible(self, visible):
+        need_redraw = self.horizontal_line.get_visible() != visible
+        self.horizontal_line.set_visible(visible)
+        self.vertical_line.set_visible(visible)
+        self.text.set_visible(visible)
+        return need_redraw
+
+    def create_new_background(self):
+        if self._creating_background:
+            # discard calls triggered from within this function
+            return
+        self._creating_background = True
+        self.set_cross_hair_visible(False)
+        self.ax.figure.canvas.draw()
+        self.background = self.ax.figure.canvas.copy_from_bbox(self.ax.bbox)
+        self.set_cross_hair_visible(True)
+        self._creating_background = False
+
+    def on_mouse_move(self, event):
+        if self.background is None:
+            self.create_new_background()
+        if not event.inaxes:
+            need_redraw = self.set_cross_hair_visible(False)
+            if need_redraw:
+                self.ax.figure.canvas.restore_region(self.background)
+                self.ax.figure.canvas.blit(self.ax.bbox)
+        else:
+            self.set_cross_hair_visible(True)
+            # update the line positions
+            x, y = event.xdata, event.ydata
+            self.horizontal_line.set_ydata(y)
+            self.vertical_line.set_xdata(x)
+            self.text.set_text('x=%1.2f, y=%1.2f' % (x, y))
+
+            self.ax.figure.canvas.restore_region(self.background)
+            self.ax.draw_artist(self.horizontal_line)
+            self.ax.draw_artist(self.vertical_line)
+            self.ax.draw_artist(self.text)
+            self.ax.figure.canvas.blit(self.ax.bbox)
+
+
+if __name__ == "__main__":
+    experiment1()

strategy_evaluation/indicators.py

@@ -0,0 +1,120 @@
+import pandas as pd
+import datetime as dt
+import matplotlib.pyplot as plt
+from util import get_data
+
+
+def author():
+    return "felixm"
+
+
+def normalize(timeseries):
+    return timeseries / timeseries.iloc[0]
+
+
+def bollinger_band(df, symbol, period=20, m=2):
+    boll_sma = df[symbol].rolling(period).mean()
+    std = df[symbol].rolling(period).std()
+    boll_up = boll_sma + m * std
+    boll_lo = boll_sma - m * std
+    df[f"{symbol}-Boll({period})-sma"] = boll_sma
+    df[f"{symbol}-Boll({period})-up"] = boll_up
+    df[f"{symbol}-Boll({period})-lo"] = boll_lo
+
+
+def sma(df, symbol, period):
+    """Adds SMA for one or multiple periods to df and returns SMAs"""
+    if type(period) is int:
+        period = [period]
+    keys = []
+    for p in period:
+        key = f"{symbol}-sma({p})"
+        df[key] = df[symbol].rolling(p).mean()
+        keys.append(key)
+    return df[keys]
+
+
+def ema(df, symbol, period):
+    """Adds a new column to the dataframe EMA(period)"""
+    df[f"{symbol}-ema({period})"] = df[symbol].ewm(span=period).mean()
+
+
+def price_sma(df, symbol, period):
+    """Calculates SMA and adds new column price divided by SMA to the df."""
+    sma = df[symbol].rolling(period).mean()
+    df[f"{symbol}-price/sma({period})"] = df[symbol] / sma
+
+
+def rsi(df, symbol, period=14):
+    """Calculates relative strength index over given period."""
+
+    def rsi(x):
+        pct = x.pct_change()
+        avg_gain = pct[pct > 0].mean()
+        avg_loss = pct[pct <= 0].abs().mean()
+        rsi = 100 - (100 /
+                     (1 + ((avg_gain / period) /
+                           (avg_loss / period))))
+        return rsi
+
+    key = f"rsi"
+    # Add one to get 'period' price changes (first change is nan).
+    period += 1
+    df[key] = df[symbol].rolling(period).apply(rsi)
+    return df[key]
+
+
+def macd(df, symbol):
+    macd = df[symbol].ewm(span=12).mean() - df[symbol].ewm(span=26).mean()
+    k1 = f"macd"
+    k2 = k1 + "-signal"
+    df[k1] = macd
+    df[k2] = macd.rolling(9).mean()
+    return df[[k1, k2]]
+
+
+def price_delta(df, symbol, period=1):
+    """Calculate delta between previous day and today."""
+    df[f"{symbol}-diff({period})"] = df[symbol].diff(periods=period)
+
+
+def test_indicators():
+    symbol = "JPM"
+
+    sd = dt.datetime(2008, 1, 1)
+    ed = dt.datetime(2009, 12, 31)
+    df = get_data([symbol], pd.date_range(sd, ed))
+    df.drop(columns=["SPY"], inplace=True)
+    df_orig = df.copy()
+    # df = normalize(df)
+
+    sma(df, symbol, 21)
+    ema(df, symbol, 21)
+    df.plot(title="21 SMA and EMA")
+    plt.savefig('figure_1.png')
+
+    df = df_orig.copy()
+    sma(df, symbol, 8)
+    price_sma(df, symbol, 8)
+    df.plot(title="SMA and price / SMA", subplots=True)
+    plt.savefig('figure_2.png')
+
+    df = df_orig.copy()
+    bollinger_band(df, symbol)
+    df.plot(title="Bollinger Band")
+    plt.savefig('figure_3.png')
+
+    df = df_orig.copy()
+    rsi(df, symbol)
+    fig, axes = plt.subplots(nrows=2, sharex=True)
+    df[symbol].plot(ax=axes[0], title="JPM price action")
+    df["JPM-rsi(14)"].plot(ax=axes[1], title="RSI")
+    plt.savefig('figure_4.png')
+
+    df = df_orig.copy()
+    macd(df, symbol)
+    fig, axes = plt.subplots(nrows=2, sharex=True)
+    df[symbol].plot(ax=axes[0], title="JPM price action")
+    df[["JPM-macd", "JPM-macd-signal"]].plot(ax=axes[1])
+    plt.savefig('figure_5.png')
+