def testPolicy(self, symbol, sd, ed, sv):
symbols = []
symbols.append(symbol)
dates = pd.date_range(sd, ed)
price = get_data(symbols, dates)
orders = []
holdings = {sym: 0 for sym in symbols}
lookback = 14
# get the indicators values
psr = get_Price_SMA_Ratio(symbols, dates, lookback=14)
bbp = get_Bollinger_Bands_ratio(symbols, dates, lookback=14)
mfi = get_MFI(symbols, dates)
for day in range(price.shape[0]):
for sym in symbols:
if sym == "SPY":
continue
# STOCK MAY BE OVERBOUGHT, INDEX DOES NOT APPEAR TO BE OVERSOLD
if (psr.ix[day, sym] < 1.05) and (
bbp.ix[day, sym] <= 0.5) and (mfi.ix[day, sym] < 0.4):
if holdings[sym] < 1000:
holdings[sym] = holdings[sym] + 1000
orders.append(
[price.index[day].date(), sym, 'BUY', 1000])
else:
holdings[sym] = holdings[sym]
orders.append(
[price.index[day].date(), sym, 'NOTHING', 0])
# Stock may be overbought, index does not appear to be overbought
elif (psr.ix[day, sym] > 0.95) and (
bbp.ix[day, sym] > 0.3) and (mfi.ix[day, sym] > 1):
if holdings[sym] > -1000:
holdings[sym] = holdings[sym] - 1000
orders.append(
[price.index[day].date(), sym, 'SELL', 1000])
else:
holdings[sym] = holdings[sym]
orders.append(
[price.index[day].date(), sym, 'NOTHING', 0])
elif (psr.ix[day, sym] >= 1) and (psr.ix[day - 1, sym] <
1) and (holdings[sym] > 0):
holdings[sym] = 0
orders.append([price.index[day].date(), sym, 'SELL', 1000])
elif (psr.ix[day, sym] <= 1) and (psr.ix[day - 1, sym] >
1) and (holdings[sym] < 0):
holdings[sym] = 0
orders.append([price.index[day].date(), sym, 'BUY', 1000])
else:
orders.append([price.index[day].date(), sym, 'NOTHING', 0])
trades_df = pd.DataFrame(orders,
columns=["Date", "Symbol", "Order", "Shares"])
trades_df.set_index('Date', inplace=True)
return trades_df
def addEvidence(self, symbol="AAPL", sd=dt.datetime(2008,1,1), ed=dt.datetime(2009,12,31), sv=100000):
syms = [symbol]
dates = pd.date_range(sd, ed)
total_prices = ut.get_data(syms, dates)
prices = total_prices[syms]
psr = get_Price_SMA_Ratio(symbols=syms,dates=dates,lookback=14)
rsi = get_RSI(symbols=syms,dates=dates,lookback=14)
mfi = get_MFI(symbols=syms,dates=dates,lookback=14)
spsr = psr.ix[:,symbol]
srsi = rsi.ix[:,symbol]
smfi = mfi.ix[:,symbol]
dpsr=self.Discretization(spsr,steps=5)
drsi=self.Discretization(srsi,steps=5)
dmfi=self.Discretization(smfi,steps=5)
prices = prices.ix[:,symbol]
dr = prices.copy()
dr[0] = np.nan
dr[1:] = (prices[1:] / prices[:-1].values) - 1
States = dpsr * 100 + drsi + dmfi
States = States.ix[:,'discretized_value']
self.learner = ql.QLearner(num_states=1000, num_actions=3)
oldprofit = 0
for iteration in range(100):
current_holding = 0
total_profit = 0
rewards = 0
for i in range(dr.shape[0] - 1):
if (i > 0):
total_profit += prices[i - 1] * current_holding * dr[i]
state = States[i]
if i == 0:
action = self.learner.querysetstate(state)
else:
action = self.learner.query(state, rewards)
holding, rewards = self.trade_actions(current_holding, action, dr[i + 1])
current_holding = holding
total_profit += prices[-2] * current_holding * dr[-1]
if total_profit == oldprofit:
break
oldprofit = total_profit
def testPolicy(self,symbol,sd,ed,sv):
symbols = []
symbols.append(symbol)
dates = pd.date_range(sd, ed)
price = get_data(symbols, dates)
orders = []
holdings = {sym: 0 for sym in symbols}
lookback = 14
psr = get_Price_SMA_Ratio(symbols,dates,lookback=14)
rsi = get_RSI(symbols,dates,lookback=14)
mfi = get_MFI(symbols,dates)
for day in range(price.shape[0]):
for sym in symbols:
if (psr.ix[day, sym] < 1.05) and (rsi.ix[day, sym] < 15) and (mfi.ix[day, sym] < 10):
if holdings[sym] < 1000:
holdings[sym] = holdings[sym] + 1000
orders.append([price.index[day].date(), sym, 'BUY', 1000])
else:
orders.append([price.index[day].date(), sym, 'NOTHING', 0])
elif (psr.ix[day, sym] > 0.95) and (rsi.ix[day, sym] > 55) and (mfi.ix[day, sym] > 40):
if holdings[sym] > -1000:
holdings[sym] = holdings[sym] - 1000
orders.append([price.index[day].date(), sym, 'SELL', 1000])
else:
orders.append([price.index[day].date(), sym, 'NOTHING', 0])
elif (psr.ix[day, sym] < 1) and (psr.ix[day-1,sym] > 1) and (holdings[sym] < 0):
holdings[sym] = holdings[sym] + 1000
orders.append([price.index[day].date(), sym, 'BUY', 1000])
elif (psr.ix[day, sym] > 1) and (psr.ix[day-1,sym] < 1) and (holdings[sym] > 0):
holdings[sym] = holdings[sym] - 1000
orders.append([price.index[day].date(), sym, 'SELL', 1000])
else:
orders.append([price.index[day].date(), sym, 'NOTHING', 0])
df_trades = pd.DataFrame(orders, columns=["Date", "Symbol", "Order", "Shares"])
df_trades.set_index('Date', inplace=True)
return df_trades
def testPolicy(self, symbol="AAPL", sd=dt.datetime(2010,1,1), ed=dt.datetime(2011,12,31), sv=100000):
dates = pd.date_range(sd, ed)
total_prices = ut.get_data([symbol], dates)
syms = [symbol]
prices = total_prices[syms]
psr = get_Price_SMA_Ratio(symbols=syms, dates=dates, lookback=14)
rsi = get_RSI(symbols=syms, dates=dates, lookback=14)
mfi = get_MFI(symbols=syms, dates=dates, lookback=14)
spsr = psr.ix[:, symbol]
srsi = rsi.ix[:, symbol]
smfi = mfi.ix[:, symbol]
dpsr = self.Discretization(spsr, steps=5)
drsi = self.Discretization(srsi, steps=5)
dmfi = self.Discretization(smfi, steps=5)
States = dpsr * 100 + drsi + dmfi
States = States.ix[:, 'discretized_value']
prices = prices.ix[:, symbol]
dr = prices.copy()
trades = prices.copy()
trades[:] = 0
dr[0] = 0
dr[1:] = (prices[1:] / prices[:-1].values) - 1
current_holding = 0
total_profit = 0
for i in range(len(dr)- 1):
if (i > 0):
total_profit += prices[i - 1] * current_holding * dr[i]
state = States[i]
action = self.learner.querysetstate(state)
holding, rewards = self.trade_actions(current_holding, action, dr[i + 1])
trades[i] = (holding - current_holding)
current_holding = holding
total_profit += prices[-2] * current_holding * dr[-1]
trades = pd.DataFrame(trades, columns=[symbol])
return trades
def addEvidence(self, symbol="JPM", \
sd=dt.datetime(2008, 1, 1), \
ed=dt.datetime(2009, 12, 31), \
sv=100000):
# add your code to do learning here
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
# get indicators
all_psr = ind.get_Price_SMA_Ratio(symbols=syms,
dates=dates,
lookback=14)
all_bbp = ind.get_Bollinger_Bands_ratio(symbols=syms,
dates=dates,
lookback=14)
all_mfi = ind.get_MFI(symbols=syms, dates=dates, lookback=14)
sym_psr = all_psr.ix[:, symbol]
sym_bbp = all_bbp.ix[:, symbol]
sym_mfi = all_mfi.ix[:, symbol]
# get daily returns
prices = prices.ix[:, symbol]
dr = prices.copy()
dr[1:] = (prices[1:] / prices[:-1].values) - 1
dr[0] = np.nan
# discretize indicators
dis_psr = self.dfDiscretize(sym_psr, steps=10)
dis_bbp = self.dfDiscretize(sym_bbp, steps=10)
dis_mfi = self.dfDiscretize(sym_mfi, steps=10)
States = dis_psr * 100 + dis_bbp * 10 + dis_mfi
States = States.ix[:, 'dis_value']
# initial qlearner
self.learner = ql.QLearner(num_states=1000, num_actions=3)
# training learner
oldprofit = 0
for iteration in range(100):
old_holding = 0
profits = 0
rewards = 0
for i in range(dr.shape[0] - 1):
if (i > 0):
profits += prices[i -
1] * old_holding * self.holdlimit * dr[i]
state = States[i]
if i == 0:
action = self.learner.querysetstate(state)
else:
action = self.learner.query(state, rewards)
holding, rewards = self.execute_action(old_holding, action,
dr[i + 1])
old_holding = holding
profits += prices[-2] * old_holding * self.holdlimit * dr[-1]
if iteration > 10:
if profits == oldprofit:
break
oldprofit = profits
# example use with new colname
volume_all = ut.get_data(syms, dates,
colname="Volume") # automatically adds SPY
volume = volume_all[syms] # only portfolio symbols
volume_SPY = volume_all['SPY'] # only SPY, for comparison later
if self.verbose: print volume
def testPolicy(self, symbol="JPM", \
sd=dt.datetime(2010, 1, 1), \
ed=dt.datetime(2011, 12, 31), \
sv=100000):
# here we build a fake set of trades
# your code should return the same sort of data
dates = pd.date_range(sd, ed)
prices_all = ut.get_data([symbol], dates) # automatically adds SPY
syms = [symbol]
prices = prices_all[syms] # only portfolio symbols
# get indicators
all_psr = ind.get_Price_SMA_Ratio(symbols=syms,
dates=dates,
lookback=14)
all_bbp = ind.get_Bollinger_Bands_ratio(symbols=syms,
dates=dates,
lookback=14)
all_mfi = ind.get_MFI(symbols=syms, dates=dates, lookback=14)
sym_psr = all_psr.ix[:, symbol]
sym_bbp = all_bbp.ix[:, symbol]
sym_mfi = all_mfi.ix[:, symbol]
# discretize indicators
dis_psr = self.dfDiscretize(sym_psr, steps=10)
dis_bbp = self.dfDiscretize(sym_bbp, steps=10)
dis_mfi = self.dfDiscretize(sym_mfi, steps=10)
States = dis_psr * 100 + dis_bbp * 10 + dis_mfi
States = States.ix[:, 'dis_value']
# get daily returns
prices = prices.ix[:, symbol]
dr = prices.copy()
dr[1:] = (prices[1:] / prices[:-1].values) - 1
dr[0] = np.nan
trades = dr.copy()
trades[:] = 0
old_holding = 0
profits = 0
for i in range(dr.shape[0] - 1):
if (i > 0):
profits += prices[i - 1] * old_holding * self.holdlimit * dr[i]
state = States[i]
action = self.learner.querysetstate(state)
holding, rewards = self.execute_action(old_holding, action,
dr[i + 1])
trades[i] = (holding - old_holding) * self.holdlimit
old_holding = holding
profits += prices[-2] * old_holding * self.holdlimit * dr[-1]
trades = pd.DataFrame(trades, columns=[symbol])
if self.verbose: print type(trades) # it better be a DataFrame!
if self.verbose: print trades
if self.verbose: print prices_all
return trades