def mean_variance(day, dt):
df = create_df('data/1707' + day +
'#ES.F.GLOB.1709#I#310#SBE##AB#t0.chi#top5.h5')
time = np.array(df["time_from_open"])
ask_price = np.array(df["a1"])
bid_price = np.array(df["b1"])
Sa_sq_sum = 0 #reference price squared
Sa_sum = 0 #sum of refernce price
Sb_sq_sum = 0
Sb_sum = 0
i = 0
while dt * i < time[-1]:
S_a = ask_price[np.searchsorted(time, dt * i)]
S_b = bid_price[np.searchsorted(time, dt * i)]
#sum of sq and sum for asks
Sa_sq_sum += S_a**2
Sa_sum += S_a
#sum of sq and sum for bids
Sb_sq_sum += S_b**2
Sb_sum += S_b
i += 1
sigma_a_sq = Sa_sq_sum / i - (Sa_sum / i)**2
sigma_b_sq = Sb_sq_sum / i - (Sb_sum / i)**2
mu_a = (ask_price[-1] - ask_price[0]) / i
mu_b = (bid_price[-1] - bid_price[0]) / i
return i, mu_a, sigma_a_sq, mu_b, sigma_b_sq
def Empirical_Inentsity(tick_size, depth, dt, day_string):
"""
tick_size: this si the tick size in the data
depth: This is asking for how many levels we want an intensity for
dt: Time interval size
day_string:the day in form 'dd' we want from out data set of SP500 in july2017
"""
#******************Import the data needed for the below.
df = create_df('data/1707' + day_string + '#ES.F.GLOB.1709#I#310#SBE##AB#t0.chi#top5.h5')
time = np.array(df["time_from_open"]) #Time from market open in nano seconds
ask = np.array(df["a1"]) #best ask price
avol = np.array(df["av1"]) #volume at best ask
avol2 = np.array(df["av2"]) #volume at level 2
bid = np.array(df["b1"])
bvol = np.array(df["bv1"])
bvol2 = np.array(df["bv2"])
#Creat vectors to stor the intensity at each distance from reference price.
Optimistic_ask_intensity = [0]*depth
Pessimistic_ask_intensity = [0]*depth
Optimistic_bid_intensity = [0]*depth
Pessimistic_bid_intensity = [0]*depth
for i in range(0,depth):
Optimistic_ask_intensity[i] = cb.Ask_Opti(time, ask,avol,avol2,i*tick_size,dt)
Pessimistic_ask_intensity[i] = cb.Ask_Pessi(time,ask,avol,avol2,i*tick_size,dt)
Optimistic_bid_intensity[i] = cb.Bid_Opti(time, bid,bvol,bvol2,i*tick_size,dt)
Pessimistic_bid_intensity[i] = cb.Bid_Pessi(time,bid,bvol,bvol2,i*tick_size,dt)
#Convert the Above lists into numoy array for math calculations
Opti_a = np.array(Optimistic_ask_intensity)
Pessi_a = np.array(Pessimistic_ask_intensity)
Opti_b = np.array(Optimistic_bid_intensity)
Pessi_b = np.array(Pessimistic_bid_intensity)
return (Opti_a, Pessi_a, Opti_b, Pessi_b)
]])))
order_hit = True
break
#THe case if the ref price becomes equal to the quote, again wait for volume
if order_hit == False:
waiting_time_sum += dt
i += 1
return count / waiting_time_sum * 60e9 #this makes the intensity per minute.
#******************************Run the Functions here*********************
#
df = create_df('data/170707#ES.F.GLOB.1709#I#310#SBE##AB#t0.chi#top5.h5')
time = np.array(df["time_from_open"])
ask = np.array(df["a1"])
avol = np.array(df["av1"])
avol2 = np.array(df["av2"])
bid = np.array(df["b1"])
bvol = np.array(df["bv1"])
bvol2 = np.array(df["bv2"])
dt = 60e9
A = Ask_Opti(time, ask, avol, avol2, 50, dt)
B = Bid_Opti(time, bid, bvol, bvol2, 50, dt)
Optimistic_ask_intensity = [0] * 5
Pessimistic_ask_intensity = [0] * 5
# Load the PAst data to back test on
data_file_list = [
'17', '18', '19', '20', '21', '24', '25', '26', '27', '28', '31'
]
Max_inv = 0 #P0aramteer to check max_inv
PnL = np.array([['No_Trades', 'Cash', "maxinv", "inv_at_T"]])
nu_max = 20000
q_max = 10
dt = 60e9
#Loop through each backtest
for file in data_file_list:
#**************************Process of the raw data**************************
df = create_df('data/1707' + str(file) +
'#ES.F.GLOB.1709#I#310#SBE##AB#t0.chi#top5.h5')
#df = df[:100000]
time = np.array(df["time_from_open"])
ask_price = np.array(df["a1"])
ask_vol = np.array(df["av1"])
ask_vol2 = np.array(df["av2"])
bid_price = np.array(df["b1"])
bid_vol = np.array(df["bv1"])
bid_vol2 = np.array(df["bv2"])
#Getting the price per minute, and then calcing rolling vriance
S_a = ask_price[0]
S_b = bid_price[0]
k = 0
from data_load import create_df
#enter the days of the files to get results for.
data_file_list = ['03','04','05','06','07','10','11','12','13','14','17',\
'18', '19','20','21','24','25','26','27','28','31']
Max_inv=0 #P0aramteer to check max_inv
PnL = np.array([['No_Trades','Cash',"maxinv"]])
for j in range(len(data_file_list)):
#**************************Process of the raw data**************************
df = create_df('data/1707'+str(data_file_list[j])+'#ES.F.GLOB.1709#I#310#SBE##AB#t0.chi#top5.h5')
df_small = df[:1000]
time = np.array(df["time_from_open"])
ask_price = np.array(df["a1"])
ask_vol = np.array(df["av1"])
ask_vol2 = np.array(df["av2"])
bid_price = np.array(df["b1"])
bid_vol = np.array(df["bv1"])
bid_vol2 = np.array(df["bv2"])
#***************************** PDE Results *************************************
#This does not really need to be inside the loop!!!
