Modularize python demo strategy library for better re-use, strategy development, and integration

[ericxor61a0c0d]

The Injective python API demo has served as a focal point for algorithmic developers to kickstart integrations with exchange applications and leverage the order-book primitives on the Injective Chain.

While some of these demo strategies are sophisticated enough to be utilized in a production environment, it can be further modularized and refactored to be reusable for traders looking to incorporate into their existing systems.

I noticed that the strategy implementations have numerous overlapping functions and helper objects that can be highly useful for all algorithm developers. A robust, well-abstracted helper and utility library can help reduce some of these strategies down to just a few hundred lines of code!

Suggestion

Structured OOP:

Listener: Manages market-specific streaming and maintains the latest state with easy-to-access market information.

• Account listener: address and subaccount state with helper functions deriving detailed balances (in margin, in order, and total/available balance), positions, and estimated portfolio value (using external price feed).
• Spot listener: market related data for 1 specified market: simple orderbook (price,quantity), trade stream, complex orderbook (includes every subaccount's quantity in a single price level) etc...
• Derivatives listener: similar to spot, except a listener that can stream relevant data for all derivatives markets including perpetuals, expiry futures, and binary options in 1 place.

Executer: Manages message sending and execution with abstract instructions

• Msg Executer: enables safe account sequence management and various types execution needs
• Account executer: manages deposit, withdrawal, transfer, bridging
• Spot executer: executes spot market related actions with abstract instructions
• Derivatives executer: executes derivatives market related actions with abstract instructions

Executer can start with simple executions and build progressively toward advanced executions from highly abstract instructions.
E.g. conversion of model-generated price/quantity to market-compliant ticks and denoms -----> auto-allocation of reduce-only orders in trade instructions, relative abstraction instead of absolute execution, TWAP & iceberg automations.

With these components implemented, an algorithm developer can spend 90% of his time implementing his model and backtesting instead of more than 60% of time building other components.

A spot market-making strategy for e.g. WETH/USDT can now be composed of:

spot listener(WETH/USDT) + account listener (MM address) + external custom data stream
-> Model: stoikov
-> spot executer (WETH/USDT) and optionally account executer (MM address)

optionally if it includes hedging:
derivatives listener (ETH/USDT Perp)
-> Model: Hedging
-> derivatives executer (ETH/USDT Perp)

Instead of needing to build all of these components, a trader only needs to implement the model and incorporate/setup the relevant executers and listeners based on his needs.

Simple back testing:

There should be a plug-and-play library for developers to easily back-test their strategies with local or sample trade data by simply changing a few lines in their existing codebase.

[BowTiedHeron]

I noticed your bounty on OpenQ for this issue. Happy to connect you with a dev that is able to help you out with this!
Are you still planning to fund this bounty when you find a contributor? @ericxor61a0c0d

[boyin0x]

Hi @BowTiedHeron, I'm currently working on it. I got approved by @ericxor61a0c0d to work on it on gitcoin bounties.

[ericxor61a0c0d]

@boyin0x what's the best way to talk to you? Feel free to drop relevant handles :)

[relaxed04]

@ericxor61a0c0d Hi Eric, I submitted a proposal for this work on gitcoin. Let me know your thoughts, I can take this to the finish line.