def recalculate_metrics(self,
symbol="JPM",
sd=dt.datetime(2008, 1, 1),
ed=dt.datetime(2009, 1, 1)):
dates = pd.date_range(sd - dt.timedelta(40), ed)
df = util.get_data([symbol], dates)[symbol]
#print df.index
normalized = df #/df.ix[sd:][0]#df.loc[sd]#df.ix[0]
sma = normalized.rolling(20).mean()
std = normalized.rolling(20).std()
bb = (normalized - (sma - std * 2)) / ((sma + std * 2) -
(sma - std * 2))
#this is very hacky
STOK_df = indicators.stochastic_oscillator_20(symbol, sd=sd, ed=ed)
STOKD = STOK_df['%D']
df = pd.DataFrame(df).assign(normalized=normalized).assign(
SMA=sma).assign(PSMA=normalized /
sma).assign(STD20=std).assign(STOKD=STOKD)[sd:]
daily_returns = df[symbol].copy()
daily_returns[1:] = (df[symbol].ix[1:] /
df[symbol].ix[:-1].values) - 1 # from lecture
daily_returns.ix[0] = 0
df = df.assign(dr=daily_returns)
self.market = df
self.current_stats = self.market
self.trades = pd.DataFrame(index=df.index)
self.trades['trade'] = 0.0 #reset trades
def __init__(self, \
variables = [0.5, 0.2, 1], \
symbol="JPM", \
sd=dt.datetime(2008, 1, 1), \
ed=dt.datetime(2009, 1, 1),
commission = 0,
impact = 0.005,
sv=100000,
verbose=False):
self.variables = variables
self.symbol = symbol
self.sv = sv
self.cash = sv
self.sd = sd
self.ed = ed
self.verbose = verbose
self.commission = commission
self.impact = impact
self.shares = 0
self.stock_price_library = None #attempt to pre-cache something
dates = pd.date_range(sd - dt.timedelta(40), ed)
df = util.get_data([symbol], dates)[symbol]
#print df.index
normalized = df #/df.ix[sd:][0]#df.loc[sd]#df.ix[0]
sma = normalized.rolling(20).mean()
std = normalized.rolling(20).std()
bb = (normalized - (sma - std * 2)) / ((sma + std * 2) -
(sma - std * 2))
#this is very hacky
STOK_df = indicators.stochastic_oscillator_20(symbol, sd=sd, ed=ed)
STOKD = STOK_df['%D']
df = pd.DataFrame(df).assign(normalized=normalized).assign(
SMA=sma).assign(PSMA=normalized /
sma).assign(STD20=std).assign(STOKD=STOKD)[sd:]
daily_returns = df[symbol].copy()
daily_returns[1:] = (df[symbol].ix[1:] /
df[symbol].ix[:-1].values) - 1 # from lecture
daily_returns.ix[0] = 0
df = df.assign(dr=daily_returns)
self.market = df
#print df
self.current_stats = self.market
#print(self.current_stats)
self.trades = pd.DataFrame(index=df.index)
self.trades['trade'] = 0.0
def testPolicy(symbol="JPM",
sd=dt.datetime(2008, 1, 1),
ed=dt.datetime(2009, 12, 31),
sv=100000):
orders_df = market.make_df_to_match_trading_days(
colnames=['Date', 'Symbol', 'Order', 'Shares'],
symbol='JPM',
sd=sd,
ed=ed)
stock_price_library = market.get_stock_prices([symbol], sd, ed)
#iterate through stock price library and short before all the drops and buy for all the gains
orders_df['Symbol'] = symbol #assign to JPM
orders_df['Order'] = "" #initialize with no orders
orders_df['Shares'] = 0.0 #initialize with no shares
orders_df['Date'] = orders_df.index
state = 0
short_price = 0.0
days_in_short = 0
#load indicators
bollinger_df = indicators.get_bollinger(symbol, sd=sd, ed=ed)
STOK_df = indicators.stochastic_oscillator_20(symbol, sd=sd, ed=ed)
SMA_df = indicators.SMA(symbol, sd=sd, ed=ed)
for row in orders_df.itertuples(index=True):
order_date = getattr(row, 'Date')
symbol = getattr(row, 'Symbol')
# print "The symbol is ", symbol
order = getattr(row, 'Order')
shares = getattr(row, 'Shares')
bollinger_high_today = bollinger_df.loc[order_date]['bb1']
bollinger_low_today = bollinger_df.loc[order_date]['bb2']
STOK_D_today = STOK_df.loc[order_date]['%D']
stock_price_today = stock_price_library.loc[order_date][symbol]
P_over_SMA_today = SMA_df.loc[order_date]['P/SMA']
total_votes = []
bb_vote = 0
bb_weight = 1
stochastic_vote = 0
stochastic_weight = 1
sma_vote = 0
sma_weight = 0.5
if stock_price_today > bollinger_high_today:
bb_vote = -1 #sell!
elif stock_price_today < bollinger_low_today:
bb_vote = 1 #buy!
if STOK_D_today < 20:
stochastic_vote = 1 #buy!
elif STOK_D_today > 80:
stochastic_vote = -1 #get out of there!
if P_over_SMA_today < 0.7:
sma_vote = -1 #looks like a selling opp
total_votes.append(bb_vote * bb_weight)
total_votes.append(stochastic_vote * stochastic_weight)
total_votes.append(sma_vote * sma_weight)
average_vote = sum(total_votes) / len(total_votes)
if state < 0:
days_in_short += 1
if state < 0 and stock_price_today > 1.2 * short_price or days_in_short > 45:
#if the price has risen 20% above our short price
#or if we have been in a short position for more than 45
#get out of our position and wait for next move.
orders_df.set_value(order_date, 'Order', 'BUY')
orders_df.set_value(order_date, 'Shares', abs(0 - state))
state = 0 #reset
days_in_short = 0
else:
if average_vote < 0: #this means I want to sell
if abs(-1000 -
state) == 0: #legality check for sale transaction
pass
else:
orders_df.set_value(order_date, 'Order', 'SELL')
orders_df.set_value(order_date, 'Shares',
abs(-1000 - state))
short_price = stock_price_today
state = -1000 #short state
if average_vote > 0: #this means I want to buy
if abs(1000 - state) == 0: #legality check for buy transaction
pass
else:
orders_df.set_value(order_date, 'Order', 'BUY')
orders_df.set_value(order_date, 'Shares',
abs(1000 - state))
state = 1000 #long state
days_in_short = 0
else:
pass
#my best policy (based on seeing the future)
return orders_df