Congratulations on completing the classroom and getting the application running, Rod. That is a real milestone. The “secret key” it is requesting is probably OANDA’s personal access token, although we should verify the exact .env variable before assuming that. For a safe initial setup: 1. Create or use an OANDA fxTrade Practice account—not a funded account. 2. Sign in to OANDA’s Account Management Portal. 3. Go to My Account → My Services → Manage API Access. 4. Generate a personal access token. 5. Copy it immediately and store it securely. Do not post or message the token to anyone. 6. Find the practice account ID inside OANDA. 7. Configure the agent to use the practice REST endpoint: 8. 9. https://api-fxpractice.oanda.com 10. Add the token and practice account ID only to the local .env file using the exact variable names expected by the application. 11. Keep order placement disabled and first test a read-only request for account information, instruments, or pricing. OANDA calls this credential a personal access token, not normally a “secret key.” Their official instructions are here: OANDA REST API authentication and practice API configuration. If you post the relevant .env.example lines and the exact error message—with all credential values removed—I’ll help you map each variable correctly. Please do not share the token, account password, full account number, or any funded-account credentials. Once it connects, I would still treat it as a practice-only build until you have a visible event log or dashboard showing exactly what the agent is observing and deciding.

You’re welcome, Rod. I am from Missouri. Since you already use OANDA, I’m wondering whether the requested “secret key” is actually an internal signing or encryption key for the Zero One application rather than an OANDA credential. If you share the exact environment-variable name and the related error—with every credential value removed—we may be able to identify what the code expects. Something named OANDA_ACCESS_TOKEN would clearly point to OANDA, while names such as SECRET_KEY, JWT_SECRET, or SESSION_SECRET usually belong to the application itself. For my own system, Threshold, I’m currently focused on U.S. equities and small-cap momentum. Robinhood is the supervised execution path, while separate market-data services provide the evidence used for discovery and decisions. The system is being built to evaluate, enter, manage, and exit one position under strict risk limits and human supervision. Alpaca would probably be my first consideration for a new API-first experiment because it gives you a cleaner development and paper-trading path. However, I wouldn’t select crypto solely because it trades continuously. A 24/7 market also means the agent must safely handle monitoring, outages, stale data, position recovery, and risk controls around the clock. I would choose the market where you already understand the price behavior and can define a narrow, testable strategy. Then choose the broker whose paper environment and API best support that strategy. The asset and strategy should drive the broker choice—not the promise that the agent can trade more often.

Rod, one lesson I learned while building my own system is that giving the “agent” priority too early can create a sophisticated way to execute an unproven idea. I would begin with one asset class, one clearly defined setup, and deterministic rules covering: - When it may trade - What evidence permits entry - What invalidates the setup - Position size and maximum loss - Exit and position-management rules - Conditions requiring no trade Then test whether those rules have any edge after spread, slippage, fees, and realistic execution timing. Only after that would I add an agent to monitor conditions, select an approved strategy, and manage the workflow. The agent should not be expected to manufacture profitability. Its value is consistently applying proven rules, refusing weak setups, controlling risk, and recording enough evidence to improve the system later. What markets and trading style were your earlier bots built around, and where did they fail—strategy selection, execution, risk control, or live results differing from the backtest?

Mark, those choices are for authenticating Claude Code—not for connecting TradingView. For an individual user, option 1 is usually the simplest. You’ll need an active Claude Pro or Max subscription, then sign in with the same Claude account you use online. Option 2 uses an Anthropic Console account and charges according to API usage. Option 3 is mainly for organizations already using Amazon Bedrock, Microsoft Foundry, or Google Vertex AI. Anthropic confirms these access options here. I would also reverse the order suggested in the first reply: 1. Build the strategy rules and decision logic. 2. Test them with fixtures or historical data. 3. Connect TradingView/MCP in read-only mode. 4. Confirm that live data is interpreted the same way as test data. 5. Paper trade and record the results. 6. Add broker execution only after the earlier stages are repeatable. An MCP connection gives the agent access to tools or data; it does not make the strategy reliable. Keeping data access separate from execution authority will also make mistakes much easier to diagnose. If you choose option 1, Claude Code’s official instructions say the Pro or Max subscription covers terminal and supported IDE use, including VS Code and Cursor. Setup details are here

I agree completely. The biggest improvement in my own workflow came when I stopped treating AI like a conversation that needed to remember everything and started treating the project like an engineered system with persistent evidence. I use ChatGPT for architecture, critical review, requirements, and test planning; Cursor for implementation inside the actual repository; and GitHub for source control, isolated branches, and preserving proven states. Threshold, the supervised agentic trading platform I’m building, has been divided into controlled phases. Each phase has a defined scope, prohibited changes, acceptance tests, and a completion report. A change does not advance merely because the code runs—it must produce repeatable evidence and preserve the safety boundaries established by earlier phases. One important lesson has been to freeze proven components instead of continually letting AI “improve” the whole system. When an area becomes tangled, we isolate it, document the contracts it must honor, and rebuild that section without carrying the old implementation forward. So my workflow now looks roughly like this: Requirement → bounded implementation → focused testing → repeated validation → evidence review → commit → freeze That structure has been far more valuable than finding a supposedly smarter model. The model can change, but the repository, specifications, tests, evidence, and decision history remain the source of truth.

Dan, the fact that the strategies fail after realistic spread and trading costs may actually mean your evaluation system is working correctly. Many common strategies appear profitable only before execution friction is included. Before searching for more strategies, I would separate three possibilities: 1. The strategy has no durable edge. 2. The edge exists, but the timeframe or instrument cannot support the trading frequency after costs. 3. The test contains a timing, pricing, spread, position-sizing, or look-ahead problem. I would examine gross expectancy versus total cost per trade, performance by market regime, trade frequency, profit concentration, maximum adverse excursion, and sensitivity to small parameter changes. Also compare results across instruments with different spreads and liquidity. I would resist lowering the test’s standard simply to produce a passing strategy. A self-improving system should be allowed to conclude that no strategy currently qualifies. That refusal is valuable evidence. The direction I’m taking with Threshold is similar, although its first job is supervised execution rather than autonomous strategy creation. It records evaluated opportunities, accepted and rejected setups, market conditions, strategy decisions, and outcomes. The longer-term research layer will use that evidence to recommend strategy changes, but it will not be permitted to rewrite or deploy live rules without separate validation and human approval. I’d be interested in seeing the exact requirements a strategy must pass in your evaluation cycle. That may tell us whether you have discovered a strategy problem, a market-selection problem, or an implementation problem.