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marketscripts.py
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marketscripts.py
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import pathlib
import datetime
import playhouse.sqlite_ext
import peewee
import sys
import io
import base64
from marketparser import Sale, Item, Equip
import matplotlib.pyplot as plt
from matplotlib.dates import DayLocator, MO, DateFormatter, MonthLocator
from matplotlib.ticker import FuncFormatter
import numpy as np
def backupDatabase():
backups_path = pathlib.Path('./backups/')
backups_path.mkdir(exist_ok=True)
filename = backups_path / ('market-backup-%s.db' % (datetime.date.today()))
db = playhouse.sqlite_ext.CSqliteExtDatabase('market.db')
db.backup_to_file(filename)
def calcSMA(average, time, cost):
# time = 1 day --> anything with less than 1 day --> delay less --> otherwise decay more
decay = 0.9
result = average * decay + cost * (1 - decay)
return result
def calcEMA(average, time, cost):
mult = 2 / (time + 1)
result = (cost - average) * mult + average
average = average * decay + cost * (1 - decay)
return result
def calcWMA(average, time, cost):
decay = 0.95
result = average * decay + cost * (1 - decay)
return result
def running_mean(x, n):
cumsum = np.cumsum(np.insert(x, 0, 0))
return (cumsum[n:] - cumsum[:-n]) / float(n)
def getVolumeData(cost_list, bins):
max_price = max(cost_list)
min_price = min(cost_list)
# get range within bins
bin_range = (max_price - min_price) / bins
bin_list = []
for cost in cost_list:
curPrice = min_price + bin_range / 2
bin_number = 0
while curPrice < cost:
curPrice += bin_range
bin_number += 1
bin_list.append(bin_number)
return bin_list
def getDailyAverage(cost_list, time_list):
daily_price_list = []
daily_time_list = []
total_daily_price = cost_list[0]
total_daily_items = 1
start_day = time_list[0]
cur_day = None
cur_price = None
for i in range(1, len(time_list)):
# get all costs from one day --> calculate average (store in new list)
cur_day = time_list[i]
cur_price = cost_list[i]
#print(cur_day.strftime("%d-%B"))
# main loop
if start_day.date() != cur_day.date():
# different day -> save everything to daily price list
#print("cur day: " + cur_day.strftime("%d-%B"))
#print("start day: " + start_day.strftime("%d-%B"))
daily_price_list.append(total_daily_price / total_daily_items)
daily_time_list.append(start_day)
# set initial stuff to this
total_daily_price = cur_price
total_daily_items = 1
start_day = cur_day
else:
total_daily_price += cur_price
total_daily_items += 1
# always add last day
daily_price_list.append(total_daily_price / total_daily_items)
daily_time_list.append(start_day)
#print(daily_price_list)
#print(daily_time_list)
#print(len(daily_price_list))
#print(len(daily_time_list))
return daily_price_list, daily_time_list
def getPlotData(cool_name):
# Exponential moving average
cost = []
time = []
# given name --> find all sales with item name (name)
# sort by date
#for result in Sale.select(Sale.cost, Sale.time, Sale.amount).join(Item).where(Item.name == name, Sale.item_id == Item.id).order_by(Sale.time.asc()):
for result in Sale.select(Sale.cost, Sale.time, Sale.amount).join(Item).where(Item.name == cool_name, Sale.item_id == Item.id).order_by(Sale.time.asc()):
#print(result.time)
time.append(datetime.datetime.fromisoformat(result.time))
cost.append(result.cost / result.amount)
return cost, time
def formatCost(num, pos):
magnitude = 0
while abs(num) >= 1000 and magnitude < 3:
magnitude += 1
num /= 1000.0
if magnitude <= 1:
return '%.f%s' % (num, ['', 'k', 'mil', 'bil'][magnitude])
return '%.1f%s' % (num, ['', 'k', 'mil', 'bil'][magnitude])
def is_outlier(points, thresh=100):
"""
Returns a boolean array with True if points are outliers and False
otherwise.
Parameters:
-----------
points : An numobservations by numdimensions array of observations
thresh : The modified z-score to use as a threshold. Observations with
a modified z-score (based on the median absolute deviation) greater
than this value will be classified as outliers.
Returns:
--------
mask : A numobservations-length boolean array.
References:
----------
Boris Iglewicz and David Hoaglin (1993), "Volume 16: How to Detect and
Handle Outliers", The ASQC Basic References in Quality Control:
Statistical Techniques, Edward F. Mykytka, Ph.D., Editor.
"""
# custom no outliers if not enough data
if points.shape[0] < 10:
return [False for i in range(points.shape[0])]
if len(points.shape) == 1:
points = points[:,None]
median = np.median(points, axis=0)
diff = np.sum((points - median)**2, axis=-1)
diff = np.sqrt(diff)
med_abs_deviation = np.median(diff)
modified_z_score = 0.6745 * diff / med_abs_deviation
return modified_z_score > thresh
# type 1: daily
# type 2: raw
# scale 1: average
# scale 2: zerod
def plotAndGenerateImage(item_name, save_location, plot_type, scale_type):
cost, time = getPlotData(item_name)
# die on nothing
if not cost:
return False
if not time:
return False
#print(cost)
#print(time)
np_cost = np.array(cost)
outliers = is_outlier(np_cost)
#for i, val in enumerate(outliers):
# if val:
# print(np_cost[i])
filtered_cost = [res for (res, check) in zip(cost, outliers) if not check]
filtered_time = [res for (res, check) in zip(time, outliers) if not check]
alldays = DayLocator() # minor ticks on the days
month = MonthLocator(bymonthday=15) # major ticks on the month
formatter = DateFormatter("%Y-%m-%d")
daily_cost, daily_time = getDailyAverage(filtered_cost, filtered_time)
mean_cost = running_mean(daily_cost, 7)
#mean_cost_14 = running_mean(daily_cost, 14)
#print(daily_cost)
#print(daily_time)
fig, ax = plt.subplots(figsize=(16, 9), dpi=100)
ax.yaxis.set_major_formatter(FuncFormatter(formatCost))
ax.xaxis.set_major_locator(month)
ax.xaxis.set_major_formatter(formatter)
ax.xaxis.set_minor_locator(alldays)
if plot_type == 0:
if len(daily_cost) == 1:
ax.plot(daily_time, daily_cost, '#7ba5ed', marker='o', label='daily average price', alpha=0.8)
else:
ax.plot(daily_time, daily_cost, '#7ba5ed', label='daily average price', alpha=0.8)
ax.plot(daily_time[3:-3], mean_cost, '#e24f4f', label='running mean (window 7)')
#ax.plot(daily_time[7:-6], mean_cost_14, label='running mean (fortnight)')
elif plot_type == 1:
if len(filtered_cost) == 1:
ax.plot(filtered_time, filtered_cost, '#7ba5ed', marker='o', label='daily average price', alpha=0.8)
else:
filtered_mean_cost = running_mean(filtered_cost, 14)
ax.plot(filtered_time, filtered_cost, '#7ba5ed', label='average price', alpha=0.8)
ax.plot(filtered_time[7:-6], filtered_mean_cost, '#e24f4f', label='running mean (window 14)')
ax.grid(True, 'major', 'y', linestyle='--', alpha=0.5)
new_bot = ax.get_ybound()[0] * 0.9
new_top = ax.get_ybound()[1] * 1.1
ax.set_ylim(top=new_top, bottom=new_bot)
plt.xlabel('date')
plt.ylabel('mesos')
plt.margins(0)
plt.title(item_name + " price")
plt.legend()
if scale_type == 0:
pass
elif scale_type == 1:
ax.set_ylim(bottom=0)
plt.savefig(save_location + '.png', bbox_inches='tight')
return True
if __name__ == "__main__":
if len(sys.argv) != 5:
print("Usage: python3 marketscripts.py name_of_item save_location")
sys.stdout.flush()
sys.exit(1)
try:
int(sys.argv[3])
int(sys.argv[4])
except ValueError as e:
sys.exit(1)
success = plotAndGenerateImage(sys.argv[1], sys.argv[2], int(sys.argv[3]), int(sys.argv[4]))
if not success:
sys.exit(1)