Workflows
End-to-end guides for the most common paths through BitPredict.
These workflows show you how BitPredict's features connect across a complete journey — from finding a strategy to deploying it, or from an idea to a live custom strategy. Each step links to the relevant how-to for detailed instructions.
Discovery → Backtest → Paper Trade → Go Live
The standard path for anyone who wants to take a pre-built BitPredict strategy from discovery to real capital deployment. Each stage validates the strategy further before increasing commitment.
Browse Strategies
Use leaderboards, AI search, and filters to surface candidates. Check Analytics Overview for market-wide context.
Reading a Strategy Detail PageAnalyse the Detail
Review all 6 tabs: equity curve, pipeline DAG, regime breakdown, trade ledger. Star favourites.
Reading a Strategy Detail PageRun a Backtest
Validate on a 1–2 year date range with realistic risk parameters. Check trade count (aim 50+).
Run Your First BacktestOptimise (optional)
Tune stop loss and take profit levels. Avoid overfitting — sort trials by Sharpe, not return %.
Run an Optimization JobPaper Trade
Assign to a Virtual Account for 2–4 weeks. Monitor daily. Compare live performance to backtest.
Set Up a Virtual AccountDemo Trade
Move to Bybit Demo testnet for real exchange execution with zero capital risk. Run for 1–2 weeks.
Connect Demo Trading AccountGo Live
Request live trading access. Assign strategy to live account with conservative initial position sizing.
Key Outcomes
- You've validated the strategy across 3 environments: backtest, simulation, and real exchange
- You have a realistic expectation of signal frequency and execution quality
- You understand the strategy's regime-conditional performance
- You know how alerts work and have them set up before going live
Idea → DAG → Optimise → Activate
How to go from a strategy idea to a live, signal-generating strategy using the visual DAG editor. This workflow covers the complete Strategy Builder lifecycle.
Browse Templates
Start from the template library. Filter by approach (trend/mean-reversion/momentum) and bar type. Open 2–3 templates to understand DAG structure before building your own.
Design the Pipeline
Sketch your signal logic: what data, which indicators, what conditions trigger entry, what rules exit. Write it in plain English before touching the canvas.
Wire the DAG
Drag nodes from the block library and connect them. Data Source → Indicators → Filters → Logic → Entry → Exit. Validate with the built-in validator.
Build a Strategy DAG EditorRun Inline Backtest
Test your DAG without leaving the builder. Check trade count and basic metrics. Iterate on the DAG if results are poor.
Optimise Parameters
Submit an optimization job. Define parameter ranges for SL, TP, indicator periods. Sort trials by Sharpe. Import the robust winner.
Run an Optimization JobOut-of-Sample Validation
Run one final backtest on a date range NOT used during optimisation. If performance holds up, you have a robust strategy.
Save as Draft
1 credit. Review one more time. Share the JSON with a colleague if you want a second opinion.
Activate
10 credits. Strategy goes live. Set up alerts immediately. Monitor for the first week closely.
Key Outcomes
- A fully custom strategy running live in BitPredict
- Confidence from multi-stage validation (backtest + optimisation + out-of-sample)
- Alerts configured so you're notified on every signal
- A reusable DAG template you can clone and modify for future strategies
Data → Train → Optimise → Integrate → Deploy
The full ML workflow: from selecting training data to deploying a custom model inside a live strategy pipeline. Requires intermediate knowledge of ML concepts.
Prepare Your Data
Go to Data → Data Quality. Find a bar type with ML Suitability score above 70. Check for adequate sample size (1,000+ bars).
Understanding Bar TypesConfigure Training Job
Select symbol, bar type, date range, model architecture (neural network recommended), features, and forecast horizon (1–5 bars ahead).
Submit Training
Cost: 1 credit per 2 minutes of compute. A standard job takes 5–20 minutes. Monitor status in Training History (Pending → Running → Completed).
Evaluate Results
Review accuracy and loss metrics. A good classification model should exceed 55% directional accuracy on the test set (random baseline = 50%).
Run ML Optimisation
Submit an ML Optimisation job to systematically tune hyperparameters. Same credit model as regular optimisation. (optional)
Open Strategy Builder
Drag an ML Model node from the block library onto the canvas. In the inspector, select your trained model from the dropdown.
Wire the Model
Connect your Data Source to the ML Model node. Connect the model's output signal to your Entry/Exit logic. The model's prediction becomes a trading signal.
Backtest & Activate
Run a backtest on the ML-augmented strategy. Validate performance. Save as draft (1 credit) then activate (10 credits).
Key Outcomes
- A trained ML model integrated into a live strategy pipeline
- Understanding of which data features improve model accuracy
- A systematic process for retraining and updating the model over time
- An ML-augmented strategy generating live signals
API Key → Integrate → Build → Automate
How a developer gets from zero to a production integration with BitPredict's REST API — fetching data, subscribing to signals, triggering backtests, and building custom tooling.
Generate API Key
Settings → API Keys → Generate. Select scopes matching your use case. Store the key as an environment variable — never hardcode it.
Access the APIRead Auth Docs
Visit /api-docs/authentication. Understand how to pass the X-API-Key header. Test with a simple curl command to the /market-data endpoint.
Install the SDK
`pip install bitpredict-python` or `npm install @bitpredict/sdk`. The SDK handles authentication, rate limiting, and response parsing automatically.
Fetch Market Data
Pull OHLCV + regime data for your target symbol and bar type. Build familiarity with the response schema before touching write endpoints.
Subscribe to Signals
Use the Alerts API or poll the /strategies/{id}/signals endpoint. Design your application to handle duplicate signals gracefully (idempotency).
Trigger Backtests
POST to /backtests with your strategy config. Poll the status endpoint until completed. Retrieve results and store them in your own database.
Handle Rate Limits
Implement exponential backoff: on 429 response, wait `min(2^retry_count, 60)` seconds before retrying. Check X-RateLimit-Reset header.
Monitor & Maintain
Subscribe to the changelog at /api-docs/changelog. Test against the sandbox environment before deploying API upgrades to production.
Key Outcomes
- A working API integration with authenticated requests
- Signal delivery wired into your own application or notification system
- Backtests triggerable programmatically from your tooling
- Rate limit handling that won't break under load