Inspiration
Money basically.
What it does
Make the market (reducing the bid-ask spread) while making us money
How we built it
python and using their interface and library
Strategy
- math(best bid of stocks in avg) > best offer of etf ← buy etfs, sell stocks
- demand is higher than supply ⇒ buy first, then sell
- supply is higher than demand ⇒ sell first, buy later
- math(best offer of stocks in avg) < best bid of etf ← buy stocks, sell etfs
- demand is higher than supply ⇒ buy first, then sell
- supply is higher than demand ⇒ sell first, buy later
Problems:
Problem 1
imagine scenario math(best bid of stocks) > best offer of etf so we want to buy etfs (for the lower price) and sell stocks (for a higher price).
- say there is a high supply for etfs (say 50) and a low demand for stocks (say 2). this means amount(stocks) << amount(etfs). Here it is a relatively safe trade to short sell stocks first, and then buy etfs, since we are pretty sure there will be etfs available
- Now say there is a higher demand for stocks (say 50) than a supply for etfs (say 2). this means amount(etfs) << amount(stocks). Here it is a relatively safe trade to first buy etfs and then sell stocks, since we are pretty sure someone will still buy our stocks.
- Now we come to the “risky” parts. what if the demand of stocks == supply for etfs. Here either trade we execute first, we are at high risk and very unsure if we can still sell/buy accordingly. thus we need a delta, a range, how far away we want to stay from this case! (Fist risk hyperparameter that can be learned)
Problem 2
what if we buy but can't sell for our calculated/estimated price (sell for profit) and thus take a potential loss and vice versa. Solution: include a loss tolerance (first implementation taking infinite losses), after which we discontinue hedging (then we would need to compensate and recover hedge ratio via strategy change) (Second risk parameter)
