I Built an AI Warren Buffett. Now It Trades For Me.

Read AI_HEDGE_FUND_BUFFETT_INSTALL.md completely, then install and validate the project described in it.

Work inside the current directory. Before running commands, show me:

1. The proposed installation path.
2. The detected operating system and available package tools.
3. The major installation steps.
4. Any credentials or paid data access that may be required.

Then proceed through the safe local installation steps. Follow the repository's current README and configuration files whenever they differ from assumptions in this document.

Do not enable live trading. Do not request that I paste secret API keys into chat. Use environment variables or the repository's documented environment file. Never print secret values.

Ask for confirmation before:

1. Installing system level packages.
2. Starting a paid model or data request.
3. Connecting to a brokerage account.
4. Submitting, replacing, or cancelling any paper order.
5. Making any change that could affect a live account.

Stop after validation and report what works, what remains unconfigured, and the exact command I should use for the first Buffett style analysis.

git clone https://github.com/virattt/ai-hedge-fund.git
cd ai-hedge-fund

git status
git remote -v

Repository setup summary

Runtime:
Package manager:
Install command:
Environment template:
Required data provider:
Supported model providers:
Buffett analyst location:
Analysis command:
Test or validation command:
Unresolved requirements:

Installation result

Dependency installation: PASS or FAIL
Runtime version:
Project environment path:
Warnings:
Next required action:

[ ] Model provider configured
[ ] Financial data provider configured
[ ] Environment file excluded from Git
[ ] No credentials visible in tracked files
[ ] Buffett analyst found
[ ] Analysis command found
[ ] Brokerage integration disabled
[ ] Live trading disabled

Run only the Warren Buffett analyst from the installed ai-hedge-fund project on AAPL using the latest date and data available to the configured provider.

Do not use other analyst signals. Do not place or prepare any trade.

Present the result as a Buffett style simulation, not as Warren Buffett's real opinion.

Return:

1. Verdict: bullish, neutral, or bearish.
2. Confidence from 0 to 100 percent, if supported by the project.
3. Business quality assessment.
4. Durability of the competitive advantage.
5. Management and capital allocation assessment.
6. Earnings and free cash flow observations.
7. Valuation and margin of safety.
8. The strongest reason to own it.
9. The strongest reason to avoid it.
10. Data date, data sources used by the application, and any missing inputs.

Separate facts produced from retrieved data from model interpretation. Do not invent unavailable metrics.

Run only the Warren Buffett analyst on the following tickers:

AAPL, NVDA, KO, META, TSLA

Use one consistent analysis date and the same available data standard for every company. Do not place trades.

Return a table with:

1. Rank.
2. Ticker.
3. Verdict.
4. Confidence.
5. Business predictability.
6. Competitive advantage.
7. Financial quality.
8. Valuation.
9. Margin of safety.
10. Primary concern.
11. One sentence conclusion.

After the table, explain why the highest ranked company scored better than the lowest ranked company. Clearly flag missing or incomparable data. Do not treat confidence as expected return.

Create a hypothetical 100,000 dollar portfolio using only the Warren Buffett analyst in the installed ai-hedge-fund project.

Eligible universe:
[INSERT TICKERS OR A DOCUMENTED INDEX UNIVERSE]

This is a research simulation. Do not connect to a brokerage and do not submit orders.

For each eligible company, classify it as:

1. Invest.
2. Good business, wrong price.
3. Outside the strategy.
4. Insufficient data.

Then propose a portfolio with:

1. Ticker.
2. Target weight.
3. Dollar allocation.
4. Verdict.
5. Confidence.
6. Investment thesis.
7. Valuation concern.
8. Primary risk.

Include a cash allocation. Confirm that weights total 100 percent and dollar allocations total 100,000 dollars. Explain any concentration limit you apply. Do not fabricate an analysis for companies with inadequate data.

Design and run a point in time backtest for the Warren Buffett analyst.

Start date: [YYYY-MM-DD]
End date: [YYYY-MM-DD]
Starting capital: 100000
Benchmark: S&P 500, if benchmark data is available through the configured source
Eligible universe: [INSERT UNIVERSE]
Review cadence: [INSERT CADENCE]

Before running it, explain:

1. How the test prevents lookahead bias.
2. Whether filings are selected using their publication dates.
3. Whether delisted companies and universe membership are handled point in time.
4. Which price series is used.
5. Whether dividends, transaction costs, and slippage are included.
6. Which limitations remain.

If the available data cannot support a defensible point in time test, stop and tell me what is missing.

If it can, run the test and report:

1. Total return.
2. Annualized return.
3. Benchmark return.
4. Maximum drawdown.
5. Number of holdings.
6. Number of orders or rebalances.
7. Best and worst contributors.
8. Ending value.
9. Monthly portfolio values.
10. Methodology and limitations.

Do not present the result as proof of future performance.

Create a self contained local HTML dashboard from the completed backtest results.

Include:

1. Portfolio value compared with the benchmark.
2. Total and annualized returns.
3. Maximum drawdown.
4. Ending portfolio value.
5. Holdings and target weights.
6. Best and worst contributors.
7. A monthly results table.
8. Backtest assumptions and limitations.
9. The exact analysis period and data date.

Use only the results already produced by the backtest. Do not invent missing observations. Save the dashboard inside a clearly named output directory and tell me how to open it locally.

Inspect this project for an existing Alpaca integration. Do not modify code and do not connect to any account yet.

Report:

1. Whether Alpaca support already exists.
2. Which files implement it.
3. Which environment variables it requires.
4. How paper mode is enforced.
5. How current positions and open orders are read.
6. How proposed target weights become orders.
7. What confirmation occurs before order submission.
8. What logging, retry, and failure handling exist.
9. What additional work would be required.

The design must default to paper trading. It must refuse live execution unless the code and configuration are deliberately changed after a separate confirmation.

Connect only to an Alpaca paper account using credentials stored in local environment variables.

Read the account, positions, buying power, and open orders. Do not submit, replace, or cancel anything yet.

Generate a proposed rebalance containing:

1. Current position.
2. Current weight.
3. Target weight.
4. Proposed side.
5. Proposed quantity or notional value.
6. Estimated resulting weight.
7. Reason.
8. Any validation warning.

Reject the plan if:

1. The account is not positively identified as a paper account.
2. Market data or account data is incomplete.
3. The target weights do not total 100 percent.
4. The plan exceeds available buying power.
5. An order would duplicate or conflict with an open order.
6. Any required credential is missing.

Save the proposed orders as a reviewable local file. Wait for my explicit confirmation before submitting paper orders.

Research signal
        ↓
Target portfolio
        ↓
Current account snapshot
        ↓
Proposed rebalance
        ↓
Validation checks
        ↓
Human confirmation
        ↓
Paper orders
        ↓
Execution log and reconciliation

Create a scheduled portfolio review design for this project, but do not install a scheduler yet.

The job should:

1. Run at the approved cadence.
2. Retrieve current data.
3. Run only the Warren Buffett analyst.
4. Compare current paper positions with proposed target weights.
5. Produce a dated report and proposed order file.
6. Send no orders automatically.
7. Record failures without retrying indefinitely.
8. Prevent overlapping runs.
9. Redact secrets from logs.

Show the proposed files, commands, schedule, locking method, and recovery procedure. Wait for approval before implementing it.

[ ] Repository cloned from https://github.com/virattt/ai-hedge-fund
[ ] Current README inspected
[ ] Dependencies installed using repository instructions
[ ] Environment configuration created safely
[ ] Secret files excluded from Git
[ ] Financial data connection validated
[ ] Model connection validated
[ ] Warren Buffett analyst located
[ ] Single ticker analysis completed
[ ] Output labeled as a Buffett style simulation
[ ] Data date and missing inputs disclosed
[ ] No live brokerage connection enabled
[ ] No orders submitted
[ ] Exact rerun command documented

AI Hedge Fund installation report

Project path:
Repository revision:
Runtime:
Dependency status:
Configured model provider:
Configured data provider:
Buffett analyst status:
Validation ticker:
Validation result:
Data limitations:
Estimated paid services involved:
Brokerage status: Disabled or Paper only
Live trading status: Disabled
First analysis command:
Recommended next prompt: