Cross-Platform AI Racing Bet Automation
Budget: $750 – $1,500 AUD
I need a single application that runs on both iPhone (Swift or React-Native front end) and Windows (desktop client or browser‐based interface) to automate my wagering on TAB fixed-odds markets for harness racing, gallops and greyhounds.
Core workflow
1. As soon as TAB releases a fixed price, the app must fetch the market, compare it to an AI-generated “true price” you calculate for every runner, and then cross-check against the manual price I type into a colour-coded field.
2. If the live price is equal to or shorter than my number and the runner is one I’ve pre-ticked as bettable, the program fires a single bet automatically. No multiples are required.
3. Every transaction and manual override is logged to a persistent profit-and-loss ledger so I can filter by date, code (harness, gallop, greyhound) and runner.
AI price engine
Feed in at minimum: latest form lines, gear changes, driver or rider changes, current track condition and barrier draw. I’m open to the modelling technique you prefer—gradient boosting, neural nets, etc.—as long as the final price updates in real time and the factors above remain visible for transparency.
Interface essentials
• Two native UIs, one optimised for iOS, the other for Windows, sharing a common back end.
• Colour band where I key my own price with a running book-percentage displayed underneath.
• Toggle list so I can arm/disarm individual runners before betting opens.
• Live P&L dashboard with export to CSV.
Integration & compliance
You’ll handle secure log-in and staking through the official TAB API (or reliable headless automation if an endpoint is unavailable) and respect any jurisdictional betting limits. All credentials stay encrypted client side.
Acceptance criteria
– Market polling <2 sec latency once odds post
– Correct bet execution proved on test funds
– AI price variance report matches spec factors
– P&L figures reconcile to TAB statements
Deliverables
• iOS app package
• Windows installer or web build
• Documented source code and model training notebook
• Quick-start guide and two rounds of post-launch bug support
If anything here is unclear, let’s clarify up front so we can keep the build tight and compliant.
Core workflow
1. As soon as TAB releases a fixed price, the app must fetch the market, compare it to an AI-generated “true price” you calculate for every runner, and then cross-check against the manual price I type into a colour-coded field.
2. If the live price is equal to or shorter than my number and the runner is one I’ve pre-ticked as bettable, the program fires a single bet automatically. No multiples are required.
3. Every transaction and manual override is logged to a persistent profit-and-loss ledger so I can filter by date, code (harness, gallop, greyhound) and runner.
AI price engine
Feed in at minimum: latest form lines, gear changes, driver or rider changes, current track condition and barrier draw. I’m open to the modelling technique you prefer—gradient boosting, neural nets, etc.—as long as the final price updates in real time and the factors above remain visible for transparency.
Interface essentials
• Two native UIs, one optimised for iOS, the other for Windows, sharing a common back end.
• Colour band where I key my own price with a running book-percentage displayed underneath.
• Toggle list so I can arm/disarm individual runners before betting opens.
• Live P&L dashboard with export to CSV.
Integration & compliance
You’ll handle secure log-in and staking through the official TAB API (or reliable headless automation if an endpoint is unavailable) and respect any jurisdictional betting limits. All credentials stay encrypted client side.
Acceptance criteria
– Market polling <2 sec latency once odds post
– Correct bet execution proved on test funds
– AI price variance report matches spec factors
– P&L figures reconcile to TAB statements
Deliverables
• iOS app package
• Windows installer or web build
• Documented source code and model training notebook
• Quick-start guide and two rounds of post-launch bug support
If anything here is unclear, let’s clarify up front so we can keep the build tight and compliant.
Related categories:
iPhone
Software Architecture
Objective C
Swift
Neural Networks
React Native
API Integration
AI Development