Comments (1)
As a start we tried calculating price multipliers of px(size x) / px(size 9)
for each model.
It appears there is a large variance multipliers itself: we see flat rate across present sizes, tail sizes being cheaper or more expensive than benchmark 9 prices.
Presumably flightclub functions in how many pairs remaining when deciding the price of a size. Without scraping such it'd be dangerous to just make inference with a fixed multiplier or assume sold-out sizes can be sold as the most expensive size right now.
Data analysis in arbitrage_locator/fc_price_inference.py
.
This, albeit promising, would be blocked until further volume / remaining_cnt collection from flightclub.
from sneaky.
Related Issues (20)
- strategyv2: csv / console / email report. serialization module
- strategy: 30p max bid mark HOT 1
- stockx: parsing failed HOT 2
- strategy: discrepancies in mid profit rate calc HOT 1
- feedv2: investigate match rate between du and stockx HOT 1
- feedv2: investigate du feed sizes fail to parse, data for none sizes, drop funky gender logic HOT 1
- feedv2: integrate daily query logic with merged csv. apply in stockx. HOT 1
- strategyv2: work with output of feedv2 HOT 1
- feedv2: unify time representation, records sorting, fetch du ask_price in update mode, clarify du bid_price in update mode HOT 3
- strategyv2: query stockx again if required. HOT 1
- strategyv2: investigate irrational historical transaction prices HOT 1
- feedv2: investigate data drop HOT 2
- strategyv2: pandas-ify
- feedv2: DB-ify HOT 1
- feedv2: du transaction pagination HOT 1
- accounting: analysis on previous iteration. post-trade-analysis script? HOT 3
- strategy: market data research HOT 2
- docs: engineering summary HOT 1
- accounting: management interface for accounting sheet
- 很棒的工作,但是可操作性很低。 HOT 1
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