BluChip AI MVP Developer Needed
Budget: $250 – $750 USD
I am ready to turn my BluChip AI prototype into a working MVP that automatically screens and recommends LEAPS call options. The stack is Python and Django, backed by a Postgres database, with data flowing in from IEX Cloud and Alpha Vantage.
The core tasks are:
• Build a clean Django backend that ingests end-of-day and real-time equity/option data from those two APIs, normalises it, and persists it for fast querying.
• Code a rule-based engine that applies my fundamental filters—growth, ROA, ROE, ROIC, financial strength, profitability, 10-year CAGR and pivot-point resistances—before it ever touches the option chain.
• On the options side, screen contracts for delta 0.25-0.35, open-interest ≥ 300, bid-ask spread ≤ 5 %, and IV rank ≤ 50, then tag each ticker/contract with an unambiguous PASS or FAIL.
• Integrate brokerage connectivity to both Tradier and IBKR so that any PASS result can be previewed and, eventually, routed for execution.
For the interface, a simple web dashboard is enough right now: a table of screened tickers with coloured PASS/FAIL badges and an expandable panel. When expanded, the panel should render the audit trail as clear visual graphs or charts—think Plotly, Chart.js or the library you prefer—so a user can see exactly why a contract qualified (or didn’t).
Acceptance criteria
1. All data calls to IEX Cloud and Alpha Vantage succeed or gracefully retry/log.
2. Screening engine returns identical PASS/FAIL results to my reference spreadsheet on a supplied test set.
3. Dashboard loads in under two seconds with 1,000 tickers in the table.
4. Tradier and IBKR account connections authenticate via OAuth/token flow and return live order-preview data.
5. Codebase is pushed to a private Git repository with a README covering setup, environment variables and unit-test runs.
If you have solid Django skills, experience with financial APIs, and can knock out clean, well-documented code quickly, let’s get this MVP shipped.
The core tasks are:
• Build a clean Django backend that ingests end-of-day and real-time equity/option data from those two APIs, normalises it, and persists it for fast querying.
• Code a rule-based engine that applies my fundamental filters—growth, ROA, ROE, ROIC, financial strength, profitability, 10-year CAGR and pivot-point resistances—before it ever touches the option chain.
• On the options side, screen contracts for delta 0.25-0.35, open-interest ≥ 300, bid-ask spread ≤ 5 %, and IV rank ≤ 50, then tag each ticker/contract with an unambiguous PASS or FAIL.
• Integrate brokerage connectivity to both Tradier and IBKR so that any PASS result can be previewed and, eventually, routed for execution.
For the interface, a simple web dashboard is enough right now: a table of screened tickers with coloured PASS/FAIL badges and an expandable panel. When expanded, the panel should render the audit trail as clear visual graphs or charts—think Plotly, Chart.js or the library you prefer—so a user can see exactly why a contract qualified (or didn’t).
Acceptance criteria
1. All data calls to IEX Cloud and Alpha Vantage succeed or gracefully retry/log.
2. Screening engine returns identical PASS/FAIL results to my reference spreadsheet on a supplied test set.
3. Dashboard loads in under two seconds with 1,000 tickers in the table.
4. Tradier and IBKR account connections authenticate via OAuth/token flow and return live order-preview data.
5. Codebase is pushed to a private Git repository with a README covering setup, environment variables and unit-test runs.
If you have solid Django skills, experience with financial APIs, and can knock out clean, well-documented code quickly, let’s get this MVP shipped.
Related categories:
PHP
JavaScript
Python
Data Processing
Django
PostgreSQL
Financial Analysis
API Integration