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start_result_collector.py
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start_result_collector.py
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import time
import numpy as np
from database import MongoDB, TaskList
from loguru import logger
from utils import calculate_after_fee
platforms = ["buff", "igxe", "c5", "uuyp"]
storage = MongoDB("data")
task_list = TaskList()
parse_ratio = lambda buy, sell_raw: (sell_raw * 0.85, buy / (sell_raw * 0.85))
def collect(buff_id, results):
item = storage.get_item(buff_id)
hash_name = item["hash_name"]
quick_price = item["buff_reference_price"]
age = time.time() - item.get("updated_at", 0)
elapsed = time.time() - results["start"]
if results.get("volume_data") is None:
action = "ignore"
else:
item["count_in_24"] = int(
results["volume_data"].get("volume", "0").replace(",", "")
)
if item["count_in_24"] < 2:
action = "skip"
elif results.get("order_data") is None or results.get("buff_data") is None:
action = "ignore"
else:
action = "parse"
if action == "ignore" or action == "skip":
item["weighted_ratio"] = 100 # assign lowest update priority
item["updated_at"] = int(time.time())
storage.update_item(item)
if action == "ignore":
logger.warning(
"Ignore item {:s} for error with age {:.2f} hours, time elapsed = {:.2f}",
hash_name,
age / 3600,
elapsed,
)
else:
logger.info(
"Skip item {:s} for low volume with age {:.2f} hours, time elapsed = {:.2f}",
hash_name,
age / 3600,
elapsed,
)
return
order_data = results["order_data"]
item["buy_order_list"] = list(
tuple(order[:2]) for order in order_data["buy_order_graph"][:10]
)
item["sell_order_list"] = list(
tuple(order[:2]) for order in order_data["sell_order_graph"][:10]
)
buff_data = results["buff_data"]
item["buff_sell_list"] = [
(eval(order["price"]), 0, 0) for order in buff_data["data"]["items"]
]
item["igxe_sell_list"] = []
igxe_data = results.get("igxe_data")
if igxe_data:
item["igxe_sell_list"] = [
(eval(order["unit_price"]), 0, 0) for order in igxe_data["d_list"]
]
item["c5_sell_list"] = []
c5_data = results.get("c5_data")
if c5_data:
item["c5_sell_list"] = [
(eval(order["price"]), 0, 0) for order in c5_data["data"]["list"]
]
item["uuyp_sell_list"] = []
uuyp_data = results.get("uuyp_data")
if uuyp_data and uuyp_data["Data"].get("CommodityList", []):
item["uuyp_sell_list"] = [
(eval(order["Price"]), 0, 0) for order in uuyp_data["Data"]["CommodityList"]
]
# compute ratio for each platform
if (
len(item["buff_sell_list"])
and len(item["buy_order_list"])
and len(item["sell_order_list"])
):
# parse platforms
for platform in platforms:
if len(item["{p}_sell_list".format(p=platform)]):
if quick_price > 8:
# for items with higer price, optimal := 1-st min, safe := 3-rd min
item["{p}_optimal_price".format(p=platform)] = item[
"{p}_sell_list".format(p=platform)
][0][0]
safe_index = np.argmin(
[
t[1] if t[1] else 9999
for t in item["{p}_sell_list".format(p=platform)][:3]
]
)
item["{p}_safe_price".format(p=platform)] = item[
"{p}_sell_list".format(p=platform)
][safe_index][0]
else:
# for items with lower price, optimal := avg of top-10 min, safe := optimal
item["{p}_optimal_price".format(p=platform)] = np.mean(
[t[0] for t in item["{p}_sell_list".format(p=platform)][:10]]
)
item["{p}_safe_price".format(p=platform)] = item[
"{p}_optimal_price".format(p=platform)
]
else:
# missing sell list
item["{p}_optimal_price".format(p=platform)] = 9999999
item["{p}_safe_price".format(p=platform)] = 9999999
# parse steam
safe_buy_list = [price for price, num in item["buy_order_list"] if num >= 3]
safe_buy_price_raw = (
safe_buy_list[0] if len(safe_buy_list) else item["buy_order_list"][-1][0]
)
optimal_buy_price_raw = item["buy_order_list"][0][0]
optimal_sell_price_raw = item["sell_order_list"][0][0]
item["optimal_buy_price"] = calculate_after_fee(optimal_buy_price_raw)
item["safe_buy_price"] = calculate_after_fee(safe_buy_price_raw)
item["optimal_sell_price"] = calculate_after_fee(optimal_sell_price_raw)
# just a placeholder; sell should not be safe
item["safe_sell_price"] = item["optimal_sell_price"]
# compute ratio
for safe in ["optimal", "safe"]:
for mode in ["buy", "sell"]:
for platform in platforms:
item["{p}_{s}_{m}_ratio".format(p=platform, s=safe, m=mode)] = (
item["{p}_{s}_price".format(p=platform, s=safe)]
/ item["{s}_{m}_price".format(s=safe, m=mode)]
)
optimal_buy_ratio = min(
item["{p}_optimal_buy_ratio".format(p=platform)] for platform in platforms
)
optimal_sell_ratio = min(
item["{p}_optimal_sell_ratio".format(p=platform)] for platform in platforms
)
# assign update priority
item["weighted_ratio"] = optimal_buy_ratio * 0.6 + optimal_sell_ratio * 0.4
else:
# get an empty sell/buy list, ignore unpopular items
if item["count_in_24"] > 10: # it is popular! what happened?
logger.warning(
"Find item {:s} (buff_id = {:d}) with empty order list",
item["name"],
item["buff_id"],
)
item["weighted_ratio"] = 100
item["updated_at"] = int(time.time())
# save to database
storage.update_item(item)
logger.info(
"Update item {:s}, age {:.2f} hours, time elapsed {:.2f}",
hash_name,
age / 3600,
elapsed,
)
if __name__ == "__main__":
while True:
task_ids = task_list.get_task_ids()
for task_id in task_ids:
task = task_list.get_task(task_id)
if task is None:
continue
if task["complete"]:
try:
collect(int(task_id), task)
except Exception as e:
logger.exception(e)
task_list.delete_task(task_id)
time.sleep(10) # scan every 10 secs