Canvas Workflow Overview
Learn the core concepts, node system, and data flow of BeeQuant canvas workflows.
What is a canvas workflow?
A BeeQuant canvas workflow is a visual quantitative strategy-building system. By dragging nodes onto the canvas and connecting data flows, you can build a complete quantitative trading strategy without writing code.
A workflow consists of seven core node types. Each node performs a specific function, and connections pass data between nodes to form a complete signal-processing pipeline from data acquisition to trade execution.
Configure strategies visually with drag and drop—no programming background required.
Configure each node independently and combine them flexibly for different strategy requirements.
Use built-in AI models including LightGBM, LSTM, Transformer, and LLMs.
Data flow
Data flows through the workflow in the following order. Each node processes its input and passes the result downstream:
The AI model node is optional, as indicated by the square brackets above. Traditional technical-indicator strategies that do not require machine-learning predictions can connect the data processing node directly to the strategy type node.
Node connection rules
Connections must follow specific rules so data can flow correctly. The table below lists the connection constraints for each node:
| Node | Upstream node (input) | Downstream node (output) | Description |
|---|---|---|---|
| Data source | None (starting point) | Data processing | Every workflow must begin with a data source node. |
| Data processing | Data source | AI model / Strategy type | Connect directly to a strategy node or route the data through an AI model first. |
| AI model | Data processing | Strategy type | Optional node for machine-learning predictions. |
| Strategy type | Data processing / AI model | Position management | Generates trading signals from the data. |
| Position management | Strategy type | Risk control | Controls capital allocation and take-profit/stop-loss rules. |
| Risk control | Position management | Trading engine | Monitors overall risk and triggers forced liquidation. |
| Trading engine | Risk control | None (end point) | Every workflow must end with a trading engine node. |
Connection ports use different colors. Only ports of the same color can be connected; the system automatically validates every connection.
A complete strategy workflow requires all of the following:
- • Data source → data origin
- • Data processing → feature calculation
- • Strategy type → signal generation
- • Position management → capital control
- • Risk control → risk management
- • Trading engine → order execution
Add the following node only when required by the strategy:
- • AI model → add when the strategy requires machine-learning predictions
Node details
Data source node
A required parameter-definition card for every data branchThe data source node is a parameter-definition card. It does not call exchange APIs, retrieve data, or output a DataFrame. It only defines the extraction parameters required by the downstream data extraction and processing node. It supports the two major exchanges Binance and OKX, covering spot, USDT-margined futures, and coin-margined futures markets.
Data processing node
The feature-engineering hub for technical indicators and quantitative factorsThe data processing node is the strategy's feature-engineering hub and the only node in the entire workflow that actually extracts OHLCV data. It reads the upstream data-source parameters, retrieves raw OHLCV data, and transforms it into factor features for strategy decisions or AI model training. Use the flexible expression syntax to define technical indicators.
AI model nodes
Optional nodes that add machine-learning predictionsAn AI model node uses the factors emitted by data processing as features to predict future returns or price direction. Its predictions can drive asset-selection rankings or timing signals.
Strategy groupStrategy type + Position management + Risk control
Strategy type, position management, and risk control form a bound strategy group, shown on the canvas inside a dashed outline. They must be connected in order and together implement the complete strategy logic from signal generation through risk management.
Strategy type node
The core signal generator that defines when and what to tradeThe strategy type node is the core trading-signal generator. It supports two main strategy classes: timing strategies, which use conditional expressions to decide when to enter and exit, and selection strategies, which rank factors to choose instruments.
- • Use conditional expressions to determine entries and exits
- • Best suited to one or a small number of instruments
- • Support long entry, short entry, long exit, and short exit signals
- • Can be combined with grid mode for layered position additions
- • Rank factors or model scores to select instruments
- • Periodically rerank and rebalance the selected instruments
- • Three subtypes: selection long, selection short, and long/short hedge
- • Support equal-weight and top-heavy allocation
Position management node
Controls capital allocation, entry size, and take-profit/stop-loss rulesPosition management is central to strategy execution. It controls critical parameters such as capital allocation, entry size, and take-profit and stop-loss rules, directly shaping the strategy's risk-and-return profile.
- • Fixed-percentage entries control risk per trade
- • Supports fixed take-profit/stop-loss and trailing stops
- • Available for timing and selection strategies
- • Adds positions in layers to average the entry cost
- • Supports ranging and trending modes
- • Available only for timing strategies
Risk control node
The final safety barrier protecting your capitalThe risk control node is a quantitative strategy's final safety barrier. It monitors runtime NAV—including realized and unrealized P&L while isolating pure deposits and withdrawals—using mutually exclusive initial-capital or high-water-mark drawdown modes, and forces all positions to close when the threshold is reached.
- • Runtime NAV includes realized and unrealized P&L
- • Initial-capital and high-water-mark modes are mutually exclusive
- • Defaults: 20% from initial capital; 25% from the high-water mark
- • Pauses strategy trading after liquidation
- • Waits for market conditions to stabilize
- • Helps avoid consecutive losses and emotional trading
Trading engine node
The execution terminal that converts strategy signals into ordersThe trading engine is the strategy's execution terminal, responsible for turning strategy signals into actual orders. It supports backtest mode for historical validation and live mode for real trading.
Workflow examples
The following two typical strategy workflows show how nodes are connected on the canvas:
Example 1: Traditional technical-indicator strategy (without an AI model)
Suitable for timing or selection strategies based on moving averages, MACD, RSI, and other technical indicatorsExample 2: AI prediction strategy (with an AI model)
Suitable for strategies that require machine-learning predictions, such as instrument ranking and return forecastingNext steps
Now that you understand the basics of canvas workflows, continue in the following order: