AMM Monitor Autonomous Agent

Overview

The AMM Monitor Autonomous Agent is designed to aggregate and process data from Automated Market Makers (AMMs). This agent operates in two modes:

• Pull Mode: Loads data from the gateway.
• Push Mode: Receives data directly from the AMM processes. These modes work in conjunction to ensure the agent maintains up-to-date AMM statistics, which are accessible and displayable through the frontend via dry runs.

State Maintenance

The Autonomous Agent actively receives data from AMMs and, to guarantee data consistency, it periodically pulls data from the gateway.

Handlers

Get-Candles

The handler expects input tags for the number of days (Days), the interval (Interval), and the AMM identifier (AMM). The handler outputs a JSON-encoded response containing candlestick data (open, high, low, close, volume) for the specified AMM over the given time period and interval.

Get-Overview

The handler returns a JSON-encoded response containing the current state of the monitored AMMs. This includes the current price, 24h volume, and 24h change. It accepts an Order-By tag to sort the AMMs by volume, transactions or date.

Register-Process

The 'Register-Process' handler is used to register a process to monitor a specific AMM. The handler expects input tags for the AMM identifier (AMM-Process-Id), an Owner-Id and the process identifier (Subscriber-Process-Id). Once registered the owner (wallet with Owner-Id) has to send 1 AOCred to the Dexi process to activate the subscription.

Get-Stats

The Get-Stats Action can be used to retreive statistics about a specific AMM. The handler expects input tags for the AMM identifier (AMM). The response message looks like this:

ao.send({
    Target = msg.From, 
    ['App-Name'] = 'Dexi',
    ['Payload'] = 'Stats',
    ['AMM'] = msg.Tags.AMM,
    ['Total-Volume'] = tostring(stats.total_volume),
    ['Buy-Volume'] = tostring(stats.buy_volume),
    ['Sell-Volume'] = tostring(stats.sell_volume),
    ['Buy-Count'] = tostring(stats.buy_count),
    ['Sell-Count'] = tostring(stats.sell_count),
    ['Buyers'] = tostring(stats.distinct_buyers),
    ['Sellers'] = tostring(stats.distinct_sellers),
    ['Total-Traders'] = tostring(stats.distinct_traders),
    ['Latest-Price'] = tostring(priceNow),
    ['Price-24H-Ago'] = tostring(price24HAgo),
    ['Price-6H-Ago'] = tostring(price6HAgo),
    ['Price-1H-Ago'] = tostring(price1HAgo)
})

Building the Agent

To build the agent, due to complications with the aos file loader, it is recommended to use amalg to create a single file from multiple scripts. This can be done by:

/opt/homebrew/bin/luacheck process.lua schemas.lua sqlschema.lua intervals.lua candles.lua stats.lua validation.lua indicators.lua

/opt/homebrew/bin/amalg.lua -s process.lua -o build/output.lua sqlschema intervals schemas validation candles stats indicators

npx aoform apply

In our experience amalagamations work more reliably than the aos loader (for now) and it also has the nice property of beign able to deploy with just one command.

To build & deploy on OSX do: bash build.sh