I am seeing inconsistent variable data within the debugger - has anyone else run into this? ```
from collections import deque
import pandas as pd
import numpy as np
from datetime import date def initialize(context):
#set_universe(universe.DollarVolumeUniverse(97.5, 98.0))
context.stocks = {sid(6413)}
context.historicals = {}
context.high_mark = 0
context.start = None
context.spikeStarts = {}
context.purchase_size = 10000
log.info("Testing") def calculateVols(r, dt):
interDev = r['C-C'].tail(20).std()
overnightDev = r['C-O'].tail(20).std()
intraDev = r['O-C'].tail(20).std()
interVol = interDev*15.87
overnightVol = overnightDev*18.59
intraVol = intraDev*30.5
r['interDev'][dt]=interDev
r['overnightDev'][dt]=overnightDev
r['intraDev'][dt]=intraDev
r['interVol'][dt]=interVol
r['overnightVol'][dt]=overnightVol
r['intraVol'][dt]=intraVol
intraAvg = r.intraVol.tail(20).mean()
overAvg = r.overnightVol.tail(20).mean()
if overAvg==0:
r['volRatio'][dt] = 0
else:
r['volRatio'][dt] = r.intraVol.tail(20).mean()/r.overnightVol.tail(20).mean() def clearPortfolio(context, stock):
if context.portfolio.positions[stock].amount > 0:
if stock in context.spikeStarts:
spikeDif = get_datetime() - context.spikeStarts[stock]
if spikeDif.days > 20:
log.info("Closing trade in {0} after {1}".format(stock.symbol, spikeDif.days))
order(stock, -1*context.portfolio.positions[stock].amount)
else:
log.info("Closing trade in {0} - could not tell how long it was open".format(stock.symbol))
order(stock, -1*context.portfolio.positions[stock].amount)
def executeTrades(context, stock):
#log.info("Checking on trade")
pos = context.portfolio.positions[stock]
if pos.amount == 0.0:
#log.info("Placing order")
order(stock, context.purchase_size) def evalTrade(context, stock, hist, dt):
vr = hist.volRatio.tail(1)
interSpike = hist.interSpike.tail(1)
movAvg = hist.volRatio.tail(5).mean()
if movAvg==0:
x=0
else:
x = (vr[dt]-movAvg)/movAvg
dif = get_datetime() - context.start
#log.info(str(dif) + " days since start")
shift = 0.0
if (int(dif.days)>33):
pos = context.portfolio.positions[stock]
if interSpike > 2 or interSpike < -2:
if context.portfolio.positions[stock].amount > 0:
#log.info("Big spike of {0} happened. Covering".format(interSpike[dt]))
spikeDif = get_datetime() - context.spikeStarts[stock]
log.info("Spike of {0} happened on {1} after {2} days.".format(interSpike[dt], stock.symbol, spikeDif.days))
order(stock, -1*pos.amount)
else:
log.info("Missed spike of {0} on {1}.".format(interSpike, stock.symbol))
if (context.high_mark!=0):
shift = x/context.high_mark
#log.info(str(vr[dt]) + "," + str(movAvg))
#log.info(str(context.high_mark) + "," + str(x) + "," + str(shift))
threshold = .75 * -1
if (shift<=threshold):
#log.info("Big swing")
context.high_mark = x
log.info("Expecting big swing in {0} on {1}".format(stock.symbol, dt))
context.spikeStarts[stock] = dt
executeTrades(context, stock)
elif (np.absolute(x)>np.absolute(context.high_mark)):
#log.info("New high mark: " + str(x))
context.high_mark = x # Will be called on every trade event for the securities you specify.
def handle_data(context, data):
for stock in data:
#log.info(stock)
quote = data[stock]
dt = quote['dt']
if stock not in context.historicals:
context.start = get_datetime()
opens = history(21, '1d', 'open_price')
closes = history(21, '1d', 'close_price')
d = {'open':opens[stock], 'last':closes[stock]}
stock_history = pd.DataFrame(d)
stock_history['C-C'] = np.log(stock_history['last']/stock_history['last'].shift(1))
stock_history['C-O'] = np.log(stock_history['open']/stock_history['last'].shift(1))
stock_history['O-C'] = np.log(stock_history['last']/stock_history['open'])
stock_history['interDev'] = float()
stock_history['overnightDev'] = float()
stock_history['intraDev'] = float()
stock_history['interVol'] = None
stock_history['overnightVol'] = None
stock_history['intraVol'] = None
stock_history['interSpike'] = float()
stock_history['overnightSpike'] = float()
stock_history['intraSpike'] = float()
stock_history['volRatio'] = None
calculateVols(stock_history, dt)
context.historicals[stock] = stock_history
#log.info(context.historicals)
else:
stock_history = context.historicals[stock]
open = quote['open_price']
last = quote['close_price']
prev_close = stock_history['last'].tail(1).item()
#prev_open = stock_history.open.tail(1).item()
prev_dev = stock_history.interDev.tail(1).item()
oneDev = prev_close*prev_dev
interday = np.log(last/prev_close)
intraday = np.log(last/open)
overnight = np.log(open/prev_close)
interSpike = oneDev * (last-prev_close)
intraSpike = oneDev * (last-open)
overnightSpike = oneDev * (open-prev_close)
row = pd.Series({'last':last, 'open':open, 'C-C': interday, 'C-O': overnight, 'O-C': intraday, 'interDev': 0.0, 'overnightDev': 0.0, 'intraDev': 0.0, 'interVol': 0.0, 'overnightVol': 0.0, 'intraVol': 0.0, 'interSpike': interSpike, 'overnightSpike': overnightSpike, 'intraSpike': intraSpike, 'volRatio': 0.0})
row.name = dt
stock_history = stock_history.append(row)
calculateVols(stock_history, dt)
#log.info(row)
context.historicals[stock] = stock_history
#log.info(context.historicals)
evalTrade(context, stock, context.historicals[stock], dt)
clearPortfolio(context, stock)
#log.info("Debug") At the breakpoint at line 74 (in evalTrade()), I am evaluating the DataSeries "hist", and I get this when I look into the data series itself:
https://www.dropbox.com/s/2wfe5sz4gctm4qv/Screenshot%202015-01-26%2022.48.19.png?dl=0
When I ask for the TimeSeries property within "hist.interDev", I get different data:
https://www.dropbox.com/s/16bilkolwd0bkov/Screenshot%202015-01-26%2022.48.47.png?dl=0
It seems to me these two should be equivalent. Am I missing something?
