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4679910374
Author | SHA1 | Date |
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Felix Martin | 4679910374 | |
Felix Martin | 1798e9569e |
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@ -75,16 +75,28 @@ def get_portfolio_value(holding, prices):
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def handle_order(date, order, holding, prices, commission, impact):
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"""Process the order."""
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symbol, order, shares = order
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assert(shares > 0) # Can only buy or sell positive amount of shares.
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if shares == 0 and order == "":
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return # empty order
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if pd.isnull(shares):
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return # shares is nan
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# Allow indicating buying and selling via shares. If shares is positive we
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# buy and if it is negative we sell.
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if shares > 0 and order == "":
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order = "BUY"
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elif shares < 0 and order == "":
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order = "SELL"
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shares = abs(shares)
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adj_closing_price = prices[symbol]
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cost = shares * adj_closing_price
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# Charge commission and deduct impact penalty
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holding['cash'] -= (commission + impact * adj_closing_price * shares)
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if order == "BUY":
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if order.upper() == "BUY":
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# print(f"Buy {shares:6} of {symbol:4} on {date}")
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holding['cash'] -= cost
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holding[symbol] += shares
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elif order == "SELL":
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elif order.upper() == "SELL":
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# print(f"Sell {shares:6} of {symbol:4} on {date}")
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holding['cash'] += cost
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holding[symbol] -= shares
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@ -25,10 +25,8 @@ class BenchmarkStrategy:
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orders["Symbol"] = symbol
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orders["Order"] = ""
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orders["Shares"] = 0
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orders.iloc[0] = [symbol, "BUY", 1000]
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orders.iloc[-1] = [symbol, "SELL", 1000]
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orders = orders[orders["Shares"] != 0]
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orders.iloc[-1] = [symbol, "SELL", -1000]
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if self.verbose:
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print(type(orders)) # it better be a DataFrame!
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@ -1,6 +1,7 @@
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import datetime as dt
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import pandas as pd
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import util as ut
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import util
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import indicators
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class ManualStrategy:
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@ -20,7 +21,7 @@ class ManualStrategy:
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# example usage of the old backward compatible util function
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syms = [symbol]
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dates = pd.date_range(sd, ed)
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prices_all = ut.get_data(syms, dates) # automatically adds SPY
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prices_all = util.get_data(syms, dates) # automatically adds SPY
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prices = prices_all[syms] # only portfolio symbols
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# prices_SPY = prices_all['SPY'] # only SPY, for comparison later
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if self.verbose:
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@ -28,31 +29,60 @@ class ManualStrategy:
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# example use with new colname
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# automatically adds SPY
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volume_all = ut.get_data(syms, dates, colname="Volume")
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volume_all = util.get_data(syms, dates, colname="Volume")
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volume = volume_all[syms] # only portfolio symbols
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# volume_SPY = volume_all['SPY'] # only SPY, for comparison later
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if self.verbose:
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print(volume)
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def macd_strat(self, macd, orders):
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def strat(ser):
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m = macd.loc[ser.index]
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prev_macd, prev_signal = m.iloc[0]
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cur_macd, cur_signal = m.iloc[1]
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shares = 0
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if cur_macd < -1 and prev_macd < prev_signal and cur_macd > cur_signal:
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if self.holding == 0:
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shares = 1000
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elif self.holding == -1000:
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shares = 2000
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elif cur_macd > 1 and prev_macd > prev_signal and cur_macd < cur_signal:
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if self.holding == 0:
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shares = -1000
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elif self.holding == 1000:
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shares = -2000
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self.holding += shares
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return shares
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orders['Shares'] = orders['Shares'].rolling(2).apply(strat)
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# this method should use the existing policy and test it against new data
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def testPolicy(self, symbol="IBM",
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sd=dt.datetime(2009, 1, 1),
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ed=dt.datetime(2010, 1, 1),
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sv=10000):
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dates = pd.date_range(sd, ed)
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prices = ut.get_data([symbol], dates) # automatically adds SPY
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orders = pd.DataFrame(index=prices.index)
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self.holding = 0
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df = util.get_data([symbol], pd.date_range(sd, ed))
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df.drop(columns=["SPY"], inplace=True)
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macd = indicators.macd(df, symbol)
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orders = pd.DataFrame(index=df.index)
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orders["Symbol"] = symbol
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orders["Order"] = ""
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orders["Shares"] = 0
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self.macd_strat(macd, orders)
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# here we build a fake set of trades
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orders.iloc[0] = [symbol, "BUY", 1000]
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orders.iloc[40] = [symbol, "SELL", 1000]
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orders.iloc[41] = [symbol, "BUY", 1000]
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orders.iloc[60] = [symbol, "SELL", 2000]
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orders.iloc[61] = [symbol, "BUY", 2000]
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orders.iloc[-1] = [symbol, "SELL", 1000]
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orders = orders[orders["Shares"] != 0]
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# orders.iloc[0] = [symbol, "BUY", 1000]
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# orders.iloc[40] = [symbol, "SELL", 1000]
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# orders.iloc[41] = [symbol, "BUY", 1000]
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# orders.iloc[60] = [symbol, "SELL", 2000]
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# orders.iloc[61] = [symbol, "BUY", 2000]
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# orders.iloc[-1] = [symbol, "SELL", 1000]
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# orders = orders[orders["Shares"] != 0]
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return orders
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@ -9,22 +9,6 @@ from BenchmarkStrategy import BenchmarkStrategy
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from ManualStrategy import ManualStrategy
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def macd_strat(macd):
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def strat(x):
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print(x)
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macd['macd_trigger'] = macd.rolling(2).apply(strat)
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# for i, row in macd.iterrows():
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# if i == 0:
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# continue
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# print(row)
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# prev_macd, prev_signal, _ = row
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# cur_macd, cur_signal, _ = row
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# if cur_macd < -.5 and (prev_macd < prev_signal) \
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# and (cur_macd > cur_signal):
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# macd.iloc[i]['macd_buy_sell'] = 1
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def experiment1():
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symbol = "JPM"
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start_value = 10000
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@ -32,46 +16,33 @@ def experiment1():
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ed = dt.datetime(2009, 12, 31)
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df = util.get_data([symbol], pd.date_range(sd, ed))
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df.drop(columns=["SPY"], inplace=True)
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# df = pd.DataFrame(index=df.index)
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bs = BenchmarkStrategy()
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orders = bs.testPolicy(symbol, sd, ed, start_value)
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df["Benchmark"] = marketsim.compute_portvals(orders, start_value)
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df["Orders Benchmark"] = orders["Shares"]
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ms = ManualStrategy()
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orders = ms.testPolicy(symbol, sd, ed, start_value)
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df["Manual"] = marketsim.compute_portvals(orders, start_value)
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df["Orders Manual"] = orders["Shares"]
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# indicators.price_sma(df, symbol, 21)
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# sma = indicators.sma(df, symbol, [9, 21])
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# rsi = indicators.rsi(df, symbol)
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macd = indicators.macd(df, symbol).copy()
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# macd_strat(macd)
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fig, ax = plt.subplots(2, sharex=True)
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fig, ax = plt.subplots(4, sharex=True)
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df[symbol].plot(ax=ax[0])
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# sma.plot(ax=ax[0])
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macd.plot(ax=ax[1])
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# macd.iloc[:,0].plot(ax=ax[1])
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# rsi.plot(ax=ax[2])
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# df[["Benchmark", "Manual"]].plot(ax=ax[3])
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macd.plot(ax=ax[3])
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df[["Benchmark", "Manual"]].plot(ax=ax[1])
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df[["Orders Benchmark", "Orders Manual"]].plot(ax=ax[2])
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# XXX: Plot where we buy and sell.
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for a in ax: a.grid()
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for a in ax:
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a.grid()
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multi = MultiCursor(fig.canvas, ax, color='r', lw=0.5)
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plt.show()
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# df.plot(title="results", subplots=True)
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#sd = dt.datetime(2008, 1, 1)
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#ed = dt.datetime(2009, 12, 31)
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#df = get_data([symbol], pd.date_range(sd, ed))
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#df.drop(columns=["SPY"], inplace=True)
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# df_orig = df.copy()
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#df = indicators.normalize(df)
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#indicators.price_sma(df, symbol, 21)
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#df.plot(title="21 SMA and EMA")
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#plt.show()
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# plt.savefig('figure_1.png')
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# You may use data from other symbols (such as SPY) to inform both your
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# Manual Learner and Strategy Learner. The in-sample/development period is
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@ -79,63 +50,5 @@ def experiment1():
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# January 1, 2010 to December 31 2011.
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class BlittedCursor:
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"""
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A cross hair cursor using blitting for faster redraw.
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"""
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def __init__(self, ax):
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self.ax = ax
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self.background = None
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self.horizontal_line = ax.axhline(color='k', lw=0.8, ls='--')
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self.vertical_line = ax.axvline(color='k', lw=0.8, ls='--')
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# text location in axes coordinates
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self.text = ax.text(0.72, 0.9, '', transform=ax.transAxes)
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self._creating_background = False
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ax.figure.canvas.mpl_connect('draw_event', self.on_draw)
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def on_draw(self, event):
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self.create_new_background()
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def set_cross_hair_visible(self, visible):
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need_redraw = self.horizontal_line.get_visible() != visible
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self.horizontal_line.set_visible(visible)
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self.vertical_line.set_visible(visible)
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self.text.set_visible(visible)
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return need_redraw
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def create_new_background(self):
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if self._creating_background:
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# discard calls triggered from within this function
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return
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self._creating_background = True
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self.set_cross_hair_visible(False)
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self.ax.figure.canvas.draw()
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self.background = self.ax.figure.canvas.copy_from_bbox(self.ax.bbox)
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self.set_cross_hair_visible(True)
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self._creating_background = False
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def on_mouse_move(self, event):
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if self.background is None:
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self.create_new_background()
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if not event.inaxes:
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need_redraw = self.set_cross_hair_visible(False)
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if need_redraw:
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self.ax.figure.canvas.restore_region(self.background)
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self.ax.figure.canvas.blit(self.ax.bbox)
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else:
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self.set_cross_hair_visible(True)
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# update the line positions
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x, y = event.xdata, event.ydata
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self.horizontal_line.set_ydata(y)
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self.vertical_line.set_xdata(x)
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self.text.set_text('x=%1.2f, y=%1.2f' % (x, y))
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self.ax.figure.canvas.restore_region(self.background)
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self.ax.draw_artist(self.horizontal_line)
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self.ax.draw_artist(self.vertical_line)
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self.ax.draw_artist(self.text)
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self.ax.figure.canvas.blit(self.ax.bbox)
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if __name__ == "__main__":
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experiment1()
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Binary file not shown.
After Width: | Height: | Size: 68 KiB |
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@ -0,0 +1,3 @@
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# Report
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![First strategy based on MACD. Better than just holding](figure_1.png)
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