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graph-average-colors.py
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graph-average-colors.py
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import csv
import matplotlib.pyplot as plt
from datetime import datetime
import numpy as np
my_day = "03/01/2020 "
TIME = 0
INDEX = 1
BIDPRICE = 2
BIDVOLUME = 3
ASKPRICE = 4
ASKVOLUME = 5
SIDE = 2
PRICE = 3
VOLUME = 4
timeSeries = []
averagePriceSeries = []
bidPriceSeries = []
askPriceSeries = []
askVolumeSeries = []
bidVolumeSeries = []
tradeTimeSeries = []
tradePriceSeries = []
tradeVolumeSeries = []
currentIndex = "ESX-FUTURE"
with open('market_data.csv') as csv_file:
csv_reader = csv.reader(csv_file, delimiter=',')
line_count = 0
for row in csv_reader:
if line_count != 0:
time = datetime.strptime(my_day + row[TIME], "%m/%d/%Y %H:%M:%S")
index = row[INDEX]
bidPrice = float(row[BIDPRICE])
bidVolume = float(row[BIDVOLUME])
askPrice = float(row[ASKPRICE])
averagePrice = (bidPrice + askPrice) / 2
askVolume = float(row[ASKVOLUME])
if index == currentIndex:
timeSeries += [time]
bidVolumeSeries += [bidVolume]
averagePriceSeries += [averagePrice]
bidPriceSeries += [bidPrice]
askPriceSeries += [askPrice]
line_count += 1
with open('trades.csv') as csv_file:
csv_reader = csv.reader(csv_file, delimiter=',')
line_count = 0
for row in csv_reader:
if line_count != 0:
time = datetime.strptime(my_day + row[TIME], "%m/%d/%Y %H:%M:%S")
index = row[INDEX]
side = row[SIDE]
price = float(row[PRICE])
volume = float(row[VOLUME])
if index == currentIndex:
tradeTimeSeries += [time]
tradePriceSeries += [price]
tradeVolumeSeries += [volume]
line_count += 1
def findAverage (time):
try:
pos = timeSeries.index(time)
average = averagePriceSeries[pos]
return average
except Exception:
return False
def averageVolume (volume):
return sum(volume) / len(volume)
tradeVolumeAboveAverage = []
tradeTimeAboveAverage = []
closingPriceAboveAverage = []
tradeVolumeBellowAverage = []
tradeTimeBellowAverage = []
closingPriceBellowAverage = []
for i in range (0, len(tradeTimeSeries)):
time = tradeTimeSeries[i]
averagePrice = findAverage(time)
if averagePrice != False:
volume = tradeVolumeSeries[i]
closingPrice = tradePriceSeries[i]
averagePrice = findAverage(time)
if closingPrice >= averagePrice:
tradeTimeAboveAverage += [time]
tradeVolumeAboveAverage += [volume]
closingPriceAboveAverage += [closingPrice]
else:
tradeTimeBellowAverage += [time]
tradeVolumeBellowAverage += [volume]
closingPriceBellowAverage += [closingPrice]
def normalize(v):
norm = np.linalg.norm(v)
if norm == 0:
return v
return v / norm
metricBellowAverage = np.full((len(tradeVolumeBellowAverage)), averageVolume(tradeVolumeBellowAverage))
metricAboveAverage = np.full((len(tradeVolumeAboveAverage)), averageVolume(tradeVolumeAboveAverage))
fig = plt.figure()
ax = plt.axes()
#ax.plot(timeSeries, bidPriceSeries, label = "BID")
#ax.plot(timeSeries, askPriceSeries, label = "ASK")
ax.plot(timeSeries, averagePriceSeries, label = "AVERAGE")
#ax.scatter(tradeTimeSeries, tradePriceSeries, s = 0.00005 * np.array(tradeVolumeSeries) ** 2,color = 'r',label = "TRADES")
#ax.scatter(tradeTimeBellowAverage, closingPriceBellowAverage, s = 0.00001 * np.array(tradeVolumeBellowAverage) ** 2, color = 'r', label = "BELLOW AVERAGE")
ax.vlines(tradeTimeBellowAverage, closingPriceBellowAverage + -0.0000001 * np.array(tradeVolumeBellowAverage) ** 2 , closingPriceBellowAverage, color = 'r', label = "BELLOW AVERAGE", linewidth = 2)
ax.vlines(tradeTimeBellowAverage, closingPriceBellowAverage + -0.0000001 * metricBellowAverage ** 2 , closingPriceBellowAverage, color = 'b', label = "BELLOW AVERAGE", linewidth = 1)
#ax.scatter(tradeTimeAboveAverage, closingPriceAboveAverage, s = 10 * normalize(np.array(tradeVolumeAboveAverage) ** 2), color = 'g', label = "ABOVE AVERAGE")
ax.vlines(tradeTimeAboveAverage, closingPriceAboveAverage, closingPriceAboveAverage + 0.0000001 * np.array(tradeVolumeAboveAverage) ** 2, color = 'g', label = "ABOVE AVERAGE", linewidth = 2)
ax.vlines(tradeTimeAboveAverage, closingPriceAboveAverage, closingPriceAboveAverage + 0.0000001 * metricAboveAverage ** 2, color = 'b', label = "ABOVE AVERAGE", linewidth = 1)
plt.legend()
plt.show()