def __init__(self, filePath, ty):
'''
Parameters
------
filePath : str
The file path of the data
ty : str
type of the data, either 'trades' or 'quotes'
'''
# Read the data in
if ty == "trades":
reader = BinReader(filePath, '>QIIf', 100)
self._ts = []
self._pr = []
while reader.hasNext():
now = reader.next()
self._ts.append(now[0])
self._pr.append(now[3])
elif ty == "quotes":
reader = BinReader(filePath, '>QIIfIf', 100)
self._ts = []
self._pr = []
while reader.hasNext():
now = reader.next()
self._ts.append(now[0])
self._pr.append((now[3] + now[5]) / 2)
def sampleLengthDay(self, date):
# date format is 'YYYYMMDD'
if self._ty == 'trades':
data = BinReader(self._path, '>QIIf', 100)
elif self._ty == 'quotes':
data = BinReader(self._path, '>QIIfIf', 100)
dateStart = datetime.strptime(date, '%Y%m%d')
dateEnd = datetime.strptime(str(int(date) + 1), '%Y%m%d')
ts = [] # List to store time stampe
while data.hasNext():
t = data.next()[0] # Time stamp
ts.append(t)
for i in range(len(ts)):
t = datetime.fromtimestamp(
ts[i] / 1000) # Convert milliseconds to DateTime
ts[i] = t
sampleLength = 0 # Counter to count the sample
for time in ts:
if time > dateStart and time < dateEnd: # Condition to count the number of trades
sampleLength += 1
if time > dateEnd: # End loop if the time already passes the target day
break
data.close()
return sampleLength
def TAQNumber(self):
if self._ty == 'trades':
data = BinReader(self._path, '>QIIf', 100)
elif self._ty == 'quotes':
data = BinReader(self._path, '>QIIfIf', 100)
count = 0
while data.hasNext():
count += 1
data.next()
data.close()
return count
class TradeReturn(object):
'''
A class that take a binary trade data file and calculate the lag return
Attributes
------
_Path : str
Path to the directory of the data read
Method
------
GetReturn(tick, lagT)
Get lagT lag return on tick
'''
def __init__(self, Path):
'''
Parameters
------
Path : str
The path to the directory which contains the binary trade data files
'''
self._Path = Path
def GetReturn(self, tick, lagT):
'''
Get the lag return of a particular ticker trades
Parameters
------
tick : str
The ticker we want to compute the return from
lagT : int
The lag, in millisecond, we want to use to compute lag return
'''
# read the trade file
self._tReader = BinReader(
os.path.join(self._Path, tick + "_trades.gz"), '>QIIf', 100)
# eight hour in milisecond
eighthour = int(2.88e7)
r_lag = []
# the next time we are looking for in the list
find_t = self._tReader.getSN() + lagT
# setup
now = self._tReader.next()
# the previous time we have observed
pre_t = now[0]
# the previous price we have observed
pre_p = now[3]
# the base price we use the calculate the return
base_p = now[3]
# a time stamp to check if we go to tomorrow data
today = now[0]
# loop through the data
while self._tReader.hasNext():
now = self._tReader.next()
# get the current time stamp
t = now[0]
if t > today + eighthour:
# if the current time is after the market time,
# go to tomorrow and start again
today = t
base_p = now[3]
elif t > find_t:
# if the current time is greater than the time,
# use the previous price to calculate the lag return
r_lag.append(pre_p / base_p - 1)
base_p = pre_p
# the next time we are looking for
find_t = pre_t + lagT
elif t == find_t:
# if the current time is exactly the time we are looking for,
# calculate the return
r_lag.append(now[3] / base_p - 1)
base_p = now[3]
find_t += lagT
pre_p = now[3]
pre_t = now[0]
return r_lag
def ori_vs_adj_midQuote_MTW():
dayStartMTW = '20070910' # Start day
dayEndMTW = '20070911' # End day
parentddir1 = os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir))
parentddir2 = os.path.abspath(os.path.join(parentddir1, os.path.pardir))
oriQuoteStartMTW = TAQQuotesReader(os.path.join(parentddir2, 'R/quotes/'+dayStartMTW+'/MTW_quotes.binRQ'))
oriQuoteEndMTW = TAQQuotesReader(os.path.join(parentddir2, 'R/quotes/'+dayEndMTW+'/MTW_quotes.binRQ'))
oriPriceMTW = [] # List to store price
oriShareMTW = [] # List to store number of shares
timeList = []
# Append data to each of the lists
for i in range(oriQuoteStartMTW.getN()):
oriPriceMTW.append((oriQuoteStartMTW.getAskPrice(i) + oriQuoteStartMTW.getBidPrice(i))/2)
oriShareMTW.append((oriQuoteStartMTW.getAskSize(i) + oriQuoteStartMTW.getBidSize(i))/2)
timeList.append(1189396800000 + oriQuoteStartMTW.getMillisFromMidn(i))
for i in range(oriQuoteEndMTW.getN()):
oriPriceMTW.append((oriQuoteEndMTW.getAskPrice(i) + oriQuoteEndMTW.getBidPrice(i))/2)
oriShareMTW.append((oriQuoteEndMTW.getAskSize(i) + oriQuoteEndMTW.getBidSize(i))/2)
timeList.append(1189483200000 + oriQuoteEndMTW.getMillisFromMidn(i))
dateTimeList = []
for timeStamp in timeList:
dateTimeList.append(datetime.fromtimestamp(timeStamp/1000))
adjQuoteStartMTW = BinReader(os.path.join(os.getcwd(), 'adjusted_quotes/MTW_quotes.gz'), '>QIIfIf', 100)
ts0, _, asize0, askp, bsize0, bidp = adjQuoteStartMTW.next()
ts = []
ts.append(ts0)
share = []
share.append(int((asize0 + bsize0)/2))
price = []
price.append((askp+bidp)/2)
while adjQuoteStartMTW.hasNext():
t, _, asize, ap, bsize, bp = adjQuoteStartMTW.next()
ts.append(t)
share.append(int((asize+bsize)/2))
price.append((ap+bp)/2)
sIdx = binarySearch(ts, 0, len(ts)-1, timeList[0])
eIdx = binarySearch(ts, 0, len(ts)-1, timeList[-1])
pList = price[sIdx:eIdx+1]
sList = share[sIdx:eIdx+1]
adjQuoteStartMTW.close()
plt.figure(figsize=(9,6))
ax = plt.gca()
xfmt = mdates.DateFormatter('%m-%d %H:%M')
ax.xaxis.set_major_formatter(xfmt)
plt.plot(dateTimeList[:-6], oriPriceMTW[:-6], ls = 'None', marker = 'o', ms = 5, color = 'y', label = 'original')
plt.plot(dateTimeList[:-6], pList, ls = 'None', marker = 'x', ms = 2 , color = 'black', label = 'adjusted')
plt.xticks([datetime.strptime('2007-09-10 10:00:00', '%Y-%m-%d %H:%M:%S'),
datetime.strptime('2007-09-10 15:00:00', '%Y-%m-%d %H:%M:%S'),
datetime.strptime('2007-09-11 09:30:00', '%Y-%m-%d %H:%M:%S'),
datetime.strptime('2007-09-11 15:00:00', '%Y-%m-%d %H:%M:%S')])
plt.title('Original MTW Mid-Quote Price VS Adjusted MTW Mid-Quote Price')
plt.xlabel('Time')
plt.ylabel('Price')
plt.legend()
plt.show()
plt.figure(figsize=(9,6))
ax = plt.gca()
xfmt = mdates.DateFormatter('%m-%d %H:%M')
ax.xaxis.set_major_formatter(xfmt)
plt.plot(dateTimeList[:-6], oriShareMTW[:-6], ls = 'None', marker = 'o', ms = 5, color = 'y', label = 'original')
plt.plot(dateTimeList[:-6], sList, ls = 'None', marker = 'x', ms = 2 , color = 'black', label = 'adjusted')
plt.xticks([datetime.strptime('2007-09-10 10:00:00', '%Y-%m-%d %H:%M:%S'),
datetime.strptime('2007-09-10 15:00:00', '%Y-%m-%d %H:%M:%S'),
datetime.strptime('2007-09-11 09:30:00', '%Y-%m-%d %H:%M:%S'),
datetime.strptime('2007-09-11 15:00:00', '%Y-%m-%d %H:%M:%S')])
plt.ylim(0,200)
plt.title('Original MTW Mid-Quote Share VS Adjusted MTW Mid-Quote Share')
plt.xlabel('Time')
plt.ylabel('Number of Shares')
plt.legend()
plt.show()
def ori_vs_adj_trade_NVDA():
dayStartNVDA = '20070910' # Start day
dayEndNVDA = '20070911' # End day
parentddir1 = os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir))
parentddir2 = os.path.abspath(os.path.join(parentddir1, os.path.pardir))
oriTradeStartNVDA = TAQTradesReader(os.path.join(parentddir2, 'R/trades/'+dayStartNVDA+'/NVDA_trades.binRT'))
oriTradeEndNVDA = TAQTradesReader(os.path.join(parentddir2, 'R/trades/'+dayEndNVDA+'/NVDA_trades.binRT'))
oriPriceNVDA = [] # List to store price
oriShareNVDA = [] # List to store number of shares
timeList = [] # List to store time stamp
# Append data to each of the lists
for i in range(oriTradeStartNVDA.getN()):
oriPriceNVDA.append(oriTradeStartNVDA.getPrice(i))
oriShareNVDA.append(oriTradeStartNVDA.getSize(i))
timeList.append(1189396800000 + oriTradeStartNVDA.getTimestamp(i))
for i in range(oriTradeEndNVDA.getN()):
oriPriceNVDA.append(oriTradeEndNVDA.getPrice(i))
oriShareNVDA.append(oriTradeEndNVDA.getSize(i))
timeList.append(1189483200000 + oriTradeEndNVDA.getTimestamp(i))
# convert data format to datetime
dateTimeList = []
for timeStamp in timeList:
dateTimeList.append(datetime.fromtimestamp(timeStamp/1000))
adjTradeStartNVDA = BinReader(os.path.join(os.getcwd(), 'adjusted_trades/NVDA_trades.gz'), '>QIIf', 100)
ts0, _, size0, p0 = adjTradeStartNVDA.next()
ts = []
ts.append(ts0)
share = []
share.append(size0)
price = []
price.append(p0)
while adjTradeStartNVDA.hasNext():
t, i, s, p = adjTradeStartNVDA.next()
ts.append(t)
share.append(s)
price.append(p)
sIdx = binarySearch(ts, 0, len(ts)-1, timeList[0]) # find the start index
eIdx = binarySearch(ts, 0, len(ts)-1, timeList[-1]) # find the end index
pList = price[sIdx:eIdx+1]
sList = share[sIdx:eIdx+1]
adjTradeStartNVDA.close() # close bin reader
plt.figure(figsize=(9,6))
ax = plt.gca()
xfmt = mdates.DateFormatter('%m-%d %H:%M')
ax.xaxis.set_major_formatter(xfmt)
plt.plot(dateTimeList[:-5], oriPriceNVDA[:-5], ls = 'None', marker = 'o', ms = 5, color = 'y', label = 'original')
plt.plot(dateTimeList[:-5], pList, ls = 'None', marker = 'x', ms = 2 , color = 'black', label = 'adjusted')
plt.xticks([datetime.strptime('2007-09-10 10:00:00', '%Y-%m-%d %H:%M:%S'),
datetime.strptime('2007-09-10 15:00:00', '%Y-%m-%d %H:%M:%S'),
datetime.strptime('2007-09-11 09:30:00', '%Y-%m-%d %H:%M:%S'),
datetime.strptime('2007-09-11 15:00:00', '%Y-%m-%d %H:%M:%S')])
plt.title('Original NVDA Trade Price VS Adjusted NVDA Trade Price')
plt.xlabel('Time')
plt.ylabel('Price')
plt.legend()
plt.show()
plt.figure(figsize=(9,6))
ax = plt.gca()
xfmt = mdates.DateFormatter('%m-%d %H:%M')
ax.xaxis.set_major_formatter(xfmt)
plt.plot(dateTimeList[:-5], oriShareNVDA[:-5], ls = 'None', marker = 'o', ms = 5, color = 'y', label = 'original')
plt.plot(dateTimeList[:-5], sList, ls = 'None', marker = 'x', ms = 2 , color = 'black', label = 'adjusted')
plt.xticks([datetime.strptime('2007-09-10 10:00:00', '%Y-%m-%d %H:%M:%S'),
datetime.strptime('2007-09-10 15:00:00', '%Y-%m-%d %H:%M:%S'),
datetime.strptime('2007-09-11 09:30:00', '%Y-%m-%d %H:%M:%S'),
datetime.strptime('2007-09-11 15:00:00', '%Y-%m-%d %H:%M:%S')])
plt.ylim(0,10000)
plt.title('Original NVDA Trade Share VS Adjusted NVDA Trade Share')
plt.xlabel('Time')
plt.ylabel('Number of Shares')
plt.legend()
plt.show()
class CleanTAQ(object):
'''
A class that clean the adjusted TAQ data
'''
def __init__(self, fileDirectory, k=5, gCoeff=0.0005):
'''
Parameters
------
fileDirectory : str
Path to the sources of the adjusted TAQ data
k : int
Windows parameter for filtering model
gCoeff : float
Gamma coefficient parameter for the filtering model
'''
self._k = k
self._gCoeff = gCoeff
self._source = fileDirectory
# set the save directory
self._direct = os.path.abspath(os.path.join(os.getcwd(), os.pardir))
self._tradeSaveDirect = os.path.join(self._direct, "clean_trades")
self._quoteSaveDirect = os.path.join(self._direct, "clean_quotes")
# if the directories does not exist, create
if not os.path.isdir(self._tradeSaveDirect):
os.mkdir(self._tradeSaveDirect)
if not os.path.isdir(self._quoteSaveDirect):
os.mkdir(self._quoteSaveDirect)
def threshold(self, p, mean, std, tot_mean):
'''
Determine if the data stay or being remove
Parameters
------
p : float
current data price
mean : float
current window mean
std : float
current window std
tot_mean : float
mean of all of the data
Returns
------
: boolean
True if the data stays, False if the data goes
'''
upperbound = 2 * std + self._gCoeff * tot_mean
if np.abs(p - mean) < upperbound:
return True
else:
return False
def writer(self, fmt):
s = struct.Struct(fmt)
return s
def clean_trades(self, tick, subset=False, save=False):
'''
Clean the TAQ trades data
Parameters
------
tick : str
The ticker of the data to be cleaned
subset : boolean
If True, only clean a subset of the data
save : boolean
If True, save the data into binary files to the certain directory
Returns
------
A tuple of number of the data, time stamp, clean price
'''
# return a tuple of lists of clean trades data
# if parameter save = True, save the data into file
print("Cleaning trade", tick)
# set up the writer format
self._tReader = BinReader(
os.path.join(self._source, tick + "_trades.gz"), '>QIIf', 100)
prices = CircularArray(self._k)
if save:
# set up the writer
tw = self.writer('>QIIf')
out = gzip.open(
os.path.join(self._tradeSaveDirect, tick + "_trades.gz"), "ab")
else:
clTs = []
clIds = []
clSs = []
clPs = []
ts = []
ids = []
ss = []
ps = []
count = 0
# get the data into lists
while self._tReader.hasNext():
t, i, s, p = self._tReader.next()
ts.append(t)
ids.append(i)
ss.append(s)
ps.append(p)
count += 1
print(tick, "has ", count, "entries")
# subset the data
if subset:
stop = int(count / 65)
else:
stop = count
tot_mean = np.mean(ps)
screen_tracker = 0
threshold_tracker = 0
# initialize the filter
while threshold_tracker < self._k:
prices.add(ps[threshold_tracker])
threshold_tracker += 1
# get the current mean in the window
pm = prices.mean()
# get the std in the window
std = prices.std()
# screen the first k/2 data
while screen_tracker < int(self._k / 2) + 1:
cp = ps[screen_tracker]
# if threshold return True, keep the data
# otherwise don't take the data
if self.threshold(cp, pm, std, tot_mean):
if save:
out.write(
tw.pack(ts[screen_tracker], ids[screen_tracker],
ss[screen_tracker], ps[screen_tracker]))
else:
clTs.append(ts[screen_tracker])
clIds.append(ids[screen_tracker])
clSs.append(ss[screen_tracker])
clPs.append(ps[screen_tracker])
screen_tracker += 1
# screen the rest of the data
while screen_tracker < stop:
cp = ps[screen_tracker]
if threshold_tracker < count:
prices.add(cp)
pm = prices.mean()
std = prices.std()
if self.threshold(cp, pm, std, tot_mean):
if save:
out.write(
tw.pack(ts[screen_tracker], ids[screen_tracker],
ss[screen_tracker], ps[screen_tracker]))
else:
clTs.append(ts[screen_tracker])
clIds.append(ids[screen_tracker])
clSs.append(ss[screen_tracker])
clPs.append(ps[screen_tracker])
screen_tracker += 1
threshold_tracker += 1
if save:
out.close()
else:
return (count, clTs, clPs)
def clean_quotes(self, tick, subset=False, save=False):
print("Cleaning quote ", tick)
self._qReader = BinReader(
os.path.join(self._source, tick + "_quotes.gz"), '>QIIfIf', 100)
mPrices = CircularArray(self._k)
if save:
qw = self.writer('>QIIfIf')
out = gzip.open(
os.path.join(self._quoteSaveDirect, tick + "_quotes.gz"), "ab")
else:
clTs = []
clIds = []
clAss = []
clAps = []
clBss = []
clBps = []
ts = []
ids = []
ass = []
aps = []
bss = []
bps = []
count = 0
while self._qReader.hasNext():
t, i, asi, ap, bs, bp = self._qReader.next()
ts.append(t)
ids.append(i)
ass.append(asi)
aps.append(ap)
bss.append(bs)
bps.append(bp)
count += 1
print(tick, "has ", count, "entries")
if subset:
stop = int(count / 65)
else:
stop = count
amean = np.mean(aps)
bmean = np.mean(bps)
mid_mean = (amean + bmean) / 2
screen_tracker = 0
threshold_tracker = 0
while threshold_tracker < self._k:
mPrices.add((aps[threshold_tracker] + bps[threshold_tracker]) / 2)
threshold_tracker += 1
mpm = mPrices.mean()
mstd = mPrices.std()
while screen_tracker < int(self._k / 2) + 1:
mcp = (aps[screen_tracker] + bps[screen_tracker]) / 2
if self.threshold(mcp, mpm, mstd, mid_mean):
if save:
out.write(
qw.pack(ts[screen_tracker], ids[screen_tracker],
ass[screen_tracker], aps[screen_tracker],
bss[screen_tracker], bps[screen_tracker]))
else:
clTs.append(ts[screen_tracker])
clIds.append(ids[screen_tracker])
clAss.append(ass[screen_tracker])
clAps.append(aps[screen_tracker])
clBss.append(bss[screen_tracker])
clBps.append(bps[screen_tracker])
screen_tracker += 1
while screen_tracker < stop:
mcp = (aps[screen_tracker] + bps[screen_tracker]) / 2
if threshold_tracker < count:
mPrices.add(mcp)
mpm = mPrices.mean()
mstd = mPrices.std()
threshold_tracker += 1
if self.threshold(mcp, mpm, mstd, mid_mean):
if save:
out.write(
qw.pack(ts[screen_tracker], ids[screen_tracker],
ass[screen_tracker], aps[screen_tracker],
bss[screen_tracker], bps[screen_tracker]))
else:
clTs.append(ts[screen_tracker])
clIds.append(ids[screen_tracker])
clAss.append(ass[screen_tracker])
clAps.append(aps[screen_tracker])
clBss.append(bss[screen_tracker])
clBps.append(bps[screen_tracker])
screen_tracker += 1
if save:
out.close()
else:
return (count, clTs, clAps, clBps)
'''
from dbReaders.BinReader import BinReader
import matplotlib.pyplot as plt
import os
from datetime import datetime
if __name__ == '__main__':
# change tick1 for another stock
tick1 = "RRC"
filePath = os.path.join(os.getcwd(), "adjusted_trades",
tick1 + "_trades.gz")
tr = BinReader(filePath, '>QIIf', 100)
ts1 = []
ps1 = []
while tr.hasNext():
now = tr.next()
ts1.append(datetime.fromtimestamp(now[0] / 1000))
ps1.append(now[3])
filePath = os.path.join(
os.path.abspath(os.path.join(os.getcwd(), os.pardir)), "clean_trades",
tick1 + "_trades.gz")
tr = BinReader(filePath, '>QIIf', 100)
ts2 = []
ps2 = []
while tr.hasNext():
now = tr.next()
ts2.append(datetime.fromtimestamp(now[0] / 1000))
ps2.append(now[3])
def tradeReturnStat(self):
tradeData = BinReader(self._path, '>QIIf', 100)
day0 = '20070620' # start day
day1 = '20070621' # one day interval
dayN = datetime.strptime('20070921', '%Y%m%d')
oneDay = pd.Timedelta(1, unit="d")
days = 65 # total number of days
startDate = datetime.strptime(day0, '%Y%m%d')
endDate = datetime.strptime(day1, '%Y%m%d')
tradeReturn = []
ts = [] # List to store time stamp
pr = [] # List to store price
while tradeData.hasNext():
t, _, _, p = tradeData.next() # Time stamp
ts.append(t)
pr.append(p)
for i in range(len(ts)):
t = datetime.fromtimestamp(
ts[i] / 1000) # Convert milliseconds to DateTime
ts[i] = t
tradeData.close()
startIdx = 0 # start index
endIdx = 0 # end index
for i in range(days):
startFlag = False
endFlag = False
# Calculate indexes
for time in ts:
if time > startDate: # start index stops
startFlag = True
if startFlag == False:
startIdx += 1
if time > endDate: # end index stops and exits the loop
endFlag = True
break
if endFlag == False:
endIdx += 1
prPerChange = (pr[endIdx - 1] -
pr[startIdx]) / pr[startIdx] # daily return
if not prPerChange == 0: # No transaction on that day
tradeReturn.append(prPerChange)
if startDate < dayN: # Move to the next day
startDate = startDate + oneDay
endDate = endDate + oneDay
if startDate.weekday(
) >= 5: # If the day is Saturday, move it to Monday
startDate = startDate + oneDay + oneDay
endDate = endDate + oneDay + oneDay
# Labor day and Independent's day are holidays!
if startDate == datetime.strptime(
'20070903', '%Y%m%d') or startDate == datetime.strptime(
'20070704', '%Y%m%d'):
startDate = startDate + oneDay
endDate = endDate + oneDay
startIdx = 0
endIdx = 0
# Calculate a bunch of statistics
meanReturn = np.mean(tradeReturn) # Mean
medianReturn = np.median(tradeReturn) # Median
stdReturn = np.std(tradeReturn) # Standard deviation
mad = []
for i in range(len(tradeReturn)):
mad.append(abs(tradeReturn[i] - meanReturn))
MAD = np.median(mad) # Median absolute deviation
annualilzedMeanReturn = (
meanReturn + 1)**252 - 1 # annualized return, 252 trading days
annualilzedMedianReturn = (medianReturn + 1)**252 - 1
annualilzedStdReturn = stdReturn * sqrt(252)
annualizedMAD = (MAD + 1)**252 - 1
skewReturn = skew(tradeReturn) # Skewness
kurtReturn = kurtosis(tradeReturn) # Kurtosis
a = sorted(tradeReturn, reverse=True) # Reverse the order, descending
tenLargest = a[0:10]
b = sorted(tradeReturn) # ascending
tenSmallest = b[0:10]
maximums = np.maximum.accumulate(pr)
drawdowns = 1 - pr / maximums
maxDrawDown = np.max(drawdowns) # maximum draw down
return annualilzedMeanReturn, annualilzedMedianReturn, annualilzedStdReturn, annualizedMAD, skewReturn, kurtReturn, tenLargest, tenSmallest, maxDrawDown
def midQuoteReturnStat(self):
quoteData = BinReader(self._path, '>QIIfIf', 100)
day0 = '20070620'
day1 = '20070621'
dayN = datetime.strptime('20070921', '%Y%m%d')
oneDay = pd.Timedelta(1, unit="d")
days = 65
startDate = datetime.strptime(day0, '%Y%m%d')
endDate = datetime.strptime(day1, '%Y%m%d')
midReturn = []
ts = [] # List to store time stamp
ask = []
bid = []
while quoteData.hasNext():
t, _, _, a, _, b = quoteData.next() # Time stamp
ts.append(t)
ask.append(a)
bid.append(b)
for i in range(len(ts)):
t = datetime.fromtimestamp(
ts[i] / 1000) # Convert milliseconds to DateTime
ts[i] = t
quoteData.close()
mid = [] # Mid quote list
for i in range(len(ask)):
mid.append((ask[i] + bid[i]) / 2)
startIdx = 0
endIdx = 0
for i in range(days):
startFlag = False
endFlag = False
#
for time in ts:
if time > startDate:
startFlag = True
if startFlag == False:
startIdx += 1
if time > endDate:
endFlag = True
break
if endFlag == False:
endIdx += 1
prPerChange = (mid[endIdx - 1] - mid[startIdx]) / mid[startIdx]
if not prPerChange == 0: # No transcation on that day
midReturn.append(prPerChange)
if startDate < dayN:
startDate = startDate + oneDay
endDate = endDate + oneDay
if startDate.weekday(
) >= 5: # If the day is Saturday, move it to Monday
startDate = startDate + oneDay + oneDay
endDate = endDate + oneDay + oneDay
# Labor day and Independent's day are holidays!
if startDate == datetime.strptime(
'20070903', '%Y%m%d') or startDate == datetime.strptime(
'20070704', '%Y%m%d'):
startDate = startDate + oneDay
endDate = endDate + oneDay
startIdx = 0
endIdx = 0
meanReturn = np.mean(midReturn)
medianReturn = np.median(midReturn)
stdReturn = np.std(midReturn)
mad = []
for i in range(len(midReturn)):
mad.append(abs(midReturn[i] - meanReturn))
MAD = np.median(mad)
annualilzedMeanReturn = (
meanReturn + 1)**252 - 1 # annualized return, 252 trading days
annualilzedMedianReturn = (medianReturn + 1)**252 - 1
annualilzedStdReturn = stdReturn * sqrt(252)
annualizedMAD = (MAD + 1)**252 - 1 # median absolute deviation
skewReturn = skew(midReturn)
kurtReturn = kurtosis(midReturn)
a = sorted(midReturn, reverse=True) # descending order
tenLargest = a[0:10]
b = sorted(midReturn) # ascending order
tenSmallest = b[0:10]
# calculate maximum drawdown
maximums = np.maximum.accumulate(mid)
drawdowns = 1 - mid / maximums
maxDrawDown = np.max(drawdowns)
return annualilzedMeanReturn, annualilzedMedianReturn, annualilzedStdReturn, annualizedMAD, skewReturn, kurtReturn, tenLargest, tenSmallest, maxDrawDown