Strategy Development Guide

Configuration Guide

The Injective Trader uses a YAML configuration file to define behavior, components, and strategy parameters. The most important configuration sections to focus on are:

LogLevel
Network and MarketTickers in the Initializer under the Components section
Strategies section

Here’s a detailed breakdown of the configuration structure:

Top-Level Parameters

Exchange: Helix                # Trading exchange to use
LogLevel: INFO                 # Logging level (DEBUG, INFO, WARNING, ERROR)

Components Section

The Components section configures framework components:

Components:
  # Chain initialization and market setup
  Initializer:
    Network: mainnet           # Network to connect to (mainnet or testnet)
    MarketTickers:             # Market tickers to track (will be converted to IDs)
      - INJ/USDT PERP
      - ETH/USDT
    BotName: MyBot

  # Chain listening configuration
  ChainListener:
    ReconnectionDelay: 5       # Seconds to wait before reconnection attempts
    LargeGapThreshold: 50      # Sequence gap threshold for orderbook snapshot requests

  # Transaction broadcasting configuration
  MessageBroadcaster:
    ErrorCodesJson: config/error_codes.json   # Error code lookup for tx validation
    GranteePool:               # For authz transaction mode
      MaxPendingTxs: 5         # Maximum pending transactions per grantee
      ErrorThreshold: 3        # Consecutive errors before blocking a grantee
      BlockDuration: 300       # Seconds to block a grantee after errors
      RotationInterval: 1      # Seconds between grantee rotations
    RefreshInterval: 300       # Seconds between grant refresh checks
    Batch:                     # Transaction batching settings
      MaxBatchSize: 15         # Maximum messages per batch
      MinBatchSize: 3          # Minimum messages to trigger immediate send
      MaxGasLimit: 5000000     # Maximum gas per batch
      MaxBatchDelay: 0.5       # Maximum seconds to wait for batch completion

Note: Most users only need to take care of Network and include all the markets that they want to listen to in MarketTickers. They won’t need to modify these advanced component settings. The default values work well for most use cases.

Strategies Section

The Strategies section defines each trading strategy:

Strategies:
  SimpleStrategy:                                    # Strategy identifier (your choice)
    # Required parameters
    Name: "SimpleStrategy"                           # Strategy name (used in logs)
    Class: "SimpleStrategy"                          # [REQUIRED] Python class name to instantiate
    MarketIds:                                       # [REQUIRED] Markets to trade on
      - "0x9b9980167ecc3645ff1a5517886652d94a0825e54a77d2057cbbe3ebee015963"  # INJ/USDT PERP
    AccountAddresses:                                # [REQUIRED] Accounts to use
      - "inj1youractualaccount..."                   # (Must match private key in env)
    TradingAccount: "inj1youractualaccount..."       # [REQUIRED] Account for placing orders (Must match private key in env)

    # Optional parameters
    FeeRecipient: "inj1feerecipient..."   # Address to receive trading fees (if applicable)
    CIDPrefix: "simple_strat"              # Prefix for client order IDs
    SubaccountIds: ["0x123..."]            # Specific subaccounts to use (otherwise all available)

    # Risk management configuration [Optional]
    # You don't have to include them if you don't have your specific risk model
    Risk: "BasicRiskModel"                 # Risk model to apply (if using risk management)
    RiskConfig:                            # Risk thresholds
      DrawdownWarning: 0.1                 # 10% drawdown warning threshold
      DrawdownCritical: 0.2                # 20% drawdown critical threshold
      MarginWarning: 0.7                   # 70% margin usage warning
      MarginCritical: 0.8                  # 80% margin usage critical

Required Strategy Parameters:

Class: Must exactly match your Python class name
MarketIds: List of market IDs to trade on in this strategy (use hex format)
AccountAddresses: List of accounts to use for trading in this strategy
TradingAccount: Account used for order execution (must be in AccountAddresses) [See more details on Trading Mode Configuration ]

Recommended Parameters:

CIDPrefix: Prefix for client order IDs (helps identify your orders)
Name: Human-readable name for logs and monitoring

Custom Parameters:

You can add any custom parameters your strategy needs
All parameters under your strategy name will be available in self.config
Group related parameters under the Parameters section for clarity

Trading Mode Configuration

The framework supports two trading modes:

Direct Execution Mode

Strategies:
  SimpleStrategy:
    # Other parameters...
    TradingAccount: "inj1youraccount..."   # Account that will sign and broadcast transactions

Authorization (Authz) Mode

Strategies:
  SimpleStrategy:
    # Other parameters...
    Granter: "inj1granteraccount..."   # Account granting permission to execute trades
    Grantees:                          # Accounts that can execute trades on behalf of granter
      - "inj1grantee1..."
      - "inj1grantee2..."

Note: You must specify either TradingAccount for direct execution OR Granter and Grantees for authorization mode. The framework enforces this requirement during initialization.

RetryConfig Section

The RetryConfig section controls retry behavior for network operations:

RetryConfig:
  # Global retry settings
  DefaultRetry:
    max_attempts: 3            # Maximum retry attempts
    base_delay: 1.0            # Base delay between retries (seconds)
    max_delay: 32.0            # Maximum delay cap (seconds)
    jitter: true               # Add randomness to delay
    timeout: 30.0              # Operation timeout (seconds)
    error_threshold: 10        # Errors before circuit breaking
    error_window: 60           # Error counting window (seconds)

  # Component-specific retry settings (override defaults)
  ChainListener:
    max_attempts: 5            # More retries for chain listener
    base_delay: 2.0
    max_delay: 45.0
  MessageBroadcaster:
    max_attempts: 3
    base_delay: 1.0
    max_delay: 30.0

Note: RetryConfig has sensible defaults and typically doesn’t need customization unless you’re experiencing specific connectivity issues.

Now that we understand the overall structure, we are ready to develop custom ones!

Strategy Development Guide

Strategies in the Injective Trader follow a consistent structure based on the Strategy base class. This section explains how to build effective strategies.

Strategy Class Structure

Your strategy class inherits from the base Strategy class:

from src.core.strategy import Strategy, StrategyResult
from src.utils.enums import UpdateType, Side

class SimpleStrategy(Strategy):
    def __init__(self, logger, config):
		"""
        Initialize strategy with logger and configuration.

        Args:
            logger: Logger instance for strategy logging
            config: Strategy configuration dictionary
                Required keys:
                - MarketIds: List of market IDs to trade
                - AccountAddresses: List of account addresses to use
                Optional keys:
                - Name: Strategy name
                - Parameters: Strategy-specific parameters
                - RiskConfig: Risk management parameters
        """
        super().__init__(logger, config)

    def on_initialize(self, accounts, markets):
        """
        Initialize strategy-specific state and parameters.
        Called once before strategy starts processing updates.

        Args:
            accounts: Dictionary of account_address -> Account
            markets: Dictionary of market_id -> Market
        """
        pass

    async def _execute_strategy(self, update_type, processed_data):
        """
        Strategy-specific execution logic.

       Args:
          update_type: Type of update being processed
          processed_data: Update-specific data dictionary after handler processing
              Common fields:
              - market_id: Market identifier
              - account_address: Account address (for account updates)
              - subaccount_id: Subaccount identifier (for position/trade updates)

        Returns:
            StrategyResult:
            - orders
            - cancellations
            - margin updates
        """
        pass

Strategy Constructor (`init`)

Your strategy class can include a constructor that calls the parent class constructor:

def __init__(self, logger, config):
    super().__init__(logger, config)

    # Custom initialization that doesn't require markets or accounts
    self.custom_parameter = 42

    # Optional: Custom handler overrides
    self.handlers[UpdateType.OnOrderbook] = MyCustomOrderbookHandler(
        self.logger, self.config, self.metrics
    )

    # Optional: Custom performance metrics overrides
    self.my_metrics = MyCustomPerformanceMetrics(self.logger)

The base class constructor handles:

Parameter validation and extraction
Setting up standard metrics and handlers (See [block link] for more information on writing your own handlers)
Initializing state tracking containers
Setting up trading mode (direct or authz)

Important: The __init__ method cannot access market data or account information. Use on_initialize for operations requiring those resources.

Available properties provided by base __init__ are:

Property	Description	Source	Required
`self.name`	Strategy name used in log	From config `Name`	YES
`self.logger`	Logger instance	For strategy-specific logging	YES
`self.config`	Complete strategy configuration	Corresponding strategy subsection under `Strategies` section in config	YES
`self.market_ids`	List of market IDs interested in this strategy	From config `MarketIds`	YES
`self.account_addresses`	List of account addresses interested in this strategy	From config `AccountAddresses`	YES
`self.subaccount_ids`	List of subaccount IDs	From config `SubaccountIds`	NO
`self.markets`	Dictionary of market_id → `Market` objects	Populated during initialization	NO
`self.accounts`	Dictionary of account_address → `Account` objects	Populated during initialization	NO
`self.trading_mode`	”direct” or “authz”	Based on config	YES
`self.fee_recipient`	Fee recipient address	From config `FeeRecipient`	NO
`self.cid_prefix`	Client order ID prefix	From config `CIDPrefix`	NO
`self.metrics`	Performance tracking	For recording metrics and alerts	YES [DEFAULT]
`self.handlers`	Event handlers dictionary	UpdateType → Handler objects	YES [DEFAULT]

Initialization Method (`on_initialize`)

The on_initialize method is called once during framework startup, after markets and accounts are loaded. Purpose: Initialize strategy state and parameters Parameters:

accounts: Dictionary of account_address → Account objects
markets: Dictionary of market_id → Market objects

Returns: [Optional] StrategyResult with initial orders (if any)

def on_initialize(self, accounts, markets):
    # Now you have access to all market and account data

    # Example: Access market metadata
    for market_id in self.market_ids:
        market = markets[market_id]
        self.logger.info(f"Market {market_id} tick sizes: "
                         f"price={market.min_price_tick}, "
                         f"quantity={market.min_quantity_tick}")

    # Example: Initialize parameters that need market info
    self.avg_prices = {
        market_id: markets[market_id].orderbook.tob()[0]
        for market_id in self.market_ids
        if markets[market_id].orderbook.tob()[0]
    }

    # Example: Place initial orders
    if self.config.get("PlaceInitialOrders", False):
        result = StrategyResult()
        # Add initial orders...
        return result

    return None  # No initial orders

This method is part of the strategy initialization sequence:

Framework loads markets and accounts required by this strategy
Your on_initialize method is called with loaded data
Any returned orders are immediately submitted
The strategy moves to running state

Tip: Use on_initialize for parameter initialization that requires market or account data, and to place any initial orders needed for your strategy. For data structure information on Account and Market, see below.

Strategy Logic (`_execute_strategy` ) Method

The _execute_strategy method is a part of “Strategy Execution (execute) Method”. The base class execute method handles the complete execution flow:

Initialization check: Initializes the strategy if needed
State update: Updates the strategy’s account and market references
Data processing: Processes raw update data through the appropriate handler
Strategy execution: Calls your _execute_strategy method with processed data
Order enrichment: Adds default values to orders (fee recipient, client ID)

You rarely need to override this method. Instead, focus on implementing _execute_strategy where your custom trading logic goes: Purpose: Analyze market data and generate trading signals Parameters:

update_type: Type of update being processed [See Update Types and Corresponding Data Fields for more information]
processed_data: Handler-processed data dictionary with relevant fields

Returns: StrategyResult with orders/cancellations or None

async def _execute_strategy(self, update_type, processed_data):
    # Only respond to orderbook updates
    if update_type != UpdateType.OnOrderbook:
        return None

    # Get market data
    market_id = processed_data["market_id"]
    market = self.markets[market_id]

    # Get current prices
    bid, ask = market.orderbook.tob()
    if not bid or not ask:
        self.logger.warning(f"Incomplete orderbook for {market_id}")
        return None

    # Implement your strategy logic
    spread = (ask - bid) / bid
    if spread < self.min_spread_threshold:
        self.logger.info(f"Spread too narrow: {spread:.2%}")
        return None

    # Get subaccount for orders
    subaccount_id = self.config.get("SubaccountIds", [""])[0]
    if not subaccount_id:
        return None

    # Check current position to avoid exceeding limits
    position = self.get_position(subaccount_id, market_id)
    current_size = Decimal("0")
    if position:
        current_size = position.get("quantity", Decimal("0"))

    # Determine order parameters
    result = StrategyResult()

    # Create a new order
    if current_size < self.max_position:
        buy_order = Order(
            market_id=market_id,
            subaccount_id=subaccount_id,
            order_side=Side.BUY,
            price=bid,
            quantity=self.order_size,
            market_type=market.market_type # Required field as of v0.5.1
        )
        result.orders.append(buy_order)
        self.logger.info(f"Creating BUY order at {bid}: {self.order_size}")

    # Cancel existing orders if needed
    for order_hash in self.active_orders:
        result.cancellations.append({
            "market_id": market_id,
            "subaccount_id": subaccount_id,
            "order_hash": order_hash
        })

    return result

In _execute_strategy you can:

Filter by update type to handle specific events
Access current market data and account state
Check existing positions before placing orders
Implement custom trading logic based on market conditions
Create new orders and cancel existing ones
Update position margins for derivative markets
Log strategy decisions for monitoring and debugging

The framework will handle the execution details like transaction creation, simulation, and broadcasting based on your returned StrategyResult.

Best Practices

Initialize all parameters in on_initialize
- Get parameters from self.config
- Set default values for missing parameters
- Initialize internal state variables
Filter update types
- Only process update types your strategy cares about
- Always check for required fields in processed_data
Validate market data
- Check if bid/ask exists before using
- Verify position exists before making decisions based on it
Respect market constraints
- Round prices and quantities to market tick sizes
- Check minimum order size and notional requirements
Handle trading account properly
- Ensure trading account is in AccountAddresses
- Specify correct subaccount_id for orders
Implement proper logging
- Log strategy decisions and important events
- Use appropriate log levels (info, warning, error)
Set custom parameters
- Use the Parameters section for strategy-specific values
- Document expected parameters

This guide should give you a solid foundation for creating and configuring effective trading strategies with the framework.

Custom Handlers

The framework processes updates through specialized handlers before passing the data to your strategy. You can create custom handlers for more control over data processing.

Handler Base Class

All handlers inherit from the UpdateHandler base class:

class UpdateHandler(ABC):
    def __init__(self, logger, config, metrics):
        self.logger = logger
        self.config = config
        self.metrics = metrics

    async def process(self, **update_data) -> Dict:
        """Process update data and return processed result"""
        try:
            return await self._process_update(**update_data)
        except Exception as e:
            self.logger.error(f"Error processing update: {e}")
            return None

    @abstractmethod
    async def _process_update(self, **kwargs) -> Dict:
        """Process specific update type. To be implemented by subclasses."""
        pass

Creating a Custom Handler

To create a custom handler:

Inherit from the appropriate handler base class
Override the _process_update method
Register your handler in your strategy’s constructor

from src.core.handlers import OrderbookHandler

class MyCustomOrderbookHandler(OrderbookHandler):
    async def _process_update(self, **update_data):
        # Get basic processed data
        processed_data = await super()._process_update(**update_data)
        if not processed_data:
            return None

        # Add custom metrics
        market = update_data.get("market")
        bid, ask = market.orderbook.tob()

        if bid and ask:
            # Calculate custom metrics
            spread = ask - bid
            spread_pct = (ask - bid) / bid

            # Add to processed data
            processed_data["spread"] = spread
            processed_data["spread_pct"] = spread_pct

            # Record in metrics system
            self.metrics.add_custom_metric(f"{market.market_id}_spread", spread_pct)

        return processed_data

Registering Custom Handlers

def __init__(self, logger, config):
    super().__init__(logger, config)

    # Replace default handlers with custom implementations
    self.handlers[UpdateType.OnOrderbook] = MyCustomOrderbookHandler(
        self.logger, self.config, self.metrics
    )
    self.handlers[UpdateType.OnPosition] = MyCustomPositionHandler(
        self.logger, self.config, self.metrics
    )

Available Handler Types

The framework provides these handler types that you can extend:

Handler Class	Update Type	Purpose
`OrderbookHandler`	`UpdateType.OnOrderbook`	Process orderbook updates
`OracleHandler`	`UpdateType.OnOraclePrice`	Process oracle price updates
`PositionHandler`	`UpdateType.OnPosition`	Process position updates
`BalanceHandler`	`UpdateType.OnBankBalance`	Process balance updates
`DepositHandler`	`UpdateType.OnDeposit`	Process deposit updates
`TradeHandler`	`UpdateType.OnSpotTrade`	Process trade execution
`OrderHandler`	`UpdateType.OnSpotOrder`	Process order updates

Key Data Structure

Update Types and Corresponding Data Fields

The framework processes these main event types that your strategy can react to:

Update Type	Description	Key Data Fields
`UpdateType.OnOrderbook`	Orderbook updates	- market_id: str - market: Market object with updated orderbook - sequence: int - block_time: Optional[str]
`UpdateType.OnOraclePrice`	Oracle price updates	- market_id: str - symbol: str - price: Decimal - timestamp: int
`UpdateType.OnBankBalance`	Account balance updates	- account_address: str - account: Account object - balance: BankBalance object
`UpdateType.OnDeposit`	Subaccount deposit updates	- account_address: str - subaccount_id: str - account: Account object - deposit: Deposit object
`UpdateType.OnPosition`	Position changes	- market_id: str - account_address: str - subaccount_id: str - account: Account object - position: Position object
`UpdateType.OnSpotTrade`	Spot trade execution	- market_id: str - subaccount_id: str - account: Account object - trade: Order object representing the trade - order: Original Order object that was filled
`UpdateType.OnDerivativeTrade`	Derivative trade execution	- market_id: str - subaccount_id: str - account: Account object - trade: Order object representing the trade - order: Original Order object that was filled
`UpdateType.OnSpotOrder`	Spot order updates	- market_id: str - subaccount_id: str - account: Account object - order: Order object
`UpdateType.OnDerivativeOrder`	Derivative order updates	- market_id: str - subaccount_id: str - account: Account object - order: Order object

Strategy Result

When your strategy decides to take action, return a StrategyResult object with:

Field	Description	Content
`orders`	List of new orders to create	`[Order(...), Order(...)]`
`cancellations`	List of orders to cancel	`[{"market_id": "0x123...", "subaccount_id": "0x456...", "order_hash": "0x789..."}]`
`margin_updates`	List of margin adjustments	`[{"action": "increase", "market_id": "0x123...", "source_subaccount_id": "0x456...", "destination_subaccount_id": "0x456...", "amount": Decimal("50.0")}]`
`liquidations`	List of positions to be liquidated due to insufficient margin	`[{"market_id": "0x123...", "subaccount_id": "0x456...", "executor_subaccount_id": "0x789...", "price": price, "quantity": quantity, "margin": margin}]`

Market

Property	Type	Description	Note
`market_id`	`str`	Unique market identifier
`market_type`	`MarketType` object	Market type (SPOT, DERIVATIVE or BINARY)	Required as of v0.5.1
`market`	`BinaryOptionMarket \| DerivativeMarket \| SpotMarket`	Market object from `pyinjective`	SpotMarket: https://api.injective.exchange/#chain-exchange-for-spot-spotmarkets DerivativeMarket: https://api.injective.exchange/#chain-exchange-for-derivatives-derivativemarkets BinaryOptionMarket: https://api.injective.exchange/#chain-exchange-for-binary-options-binaryoptionsmarkets
`orderbook`	`Orderbook` object	Current market orderbook
`oracle_price`	`Decimal`	Current market oracle price
`min_price_tick`	`Decimal`	Minimum price increment
`min_quantity_tick`	`Decimal`	Minimum quantity increment
`base_oracle_price`	`Decimal`	Base token oracle price	Optional
`quote_oracle_price`	`Decimal`	Quote token oracle price	Optional
`oracle_timestamp`	`int`	Oracle price timestamp	Optional
`mark_price`	`Decimal`	Mark price for derivative	Optional

Orderbook

Property	Type	Description	Note
`sequence`	`str`	Orderbook sequence number
`bids`	`List[L2PriceLevel]`	List of bid price levels	`L2PriceLevel` : `price` and `quantity`
`asks`	`List[L2PriceLevel]`	List of ask price levels	`L2PriceLevel` : `price` and `quantity`
`tob`	`Tuple`	Top of book (bid, ask) prices
`is_health`	`bool`	Indicates if orderbook is in healthy state	`False` when sequence is 0

Account

Property	Type	Description	Note
`private_key`	`PrivateKey`	Injective private key	Not directly accessible
`public_key`	`PublicKey`	Corresponding public key
`address`	`Address`	Account address object	Contains sequence information
`account_address`	`str`	Bech32 address string	Format: “inj1…”
`bank_balances`	`Dict[str, BankBalance]`	Token balances in account	Key: denom
`balances`	`Dict[str, Balance]`	Alternative balance representation	Key: denom
`subaccounts`	`Dict[str, SubAccount]`	Subaccounts owned by this account	Key: subaccount_id
`sequence`	`int`	Transaction sequence number	Property that accesses address.sequence

BankBalance

Property	Type	Description	Note
`denom`	`str`	Token denomination	e.g., “inj”, “peggy0x…”
`amount`	`Decimal`	Token amount	Human-readable format

Balance

Property	Type	Description	Note
`denom`	`str`	Token denomination	e.g., “inj”, “peggy0x…”
`total`	`Decimal`	Total balance
`available`	`Decimal`	Available balance	Total minus locked amounts

SubAccount

Property	Type	Description	Note
`subaccount_id`	`str`	Unique subaccount identifier	Format: “0x…”
`portfolio`	`Dict[str, Deposit]`	Token deposits in subaccount	Key: denom
`positions`	`Dict[str, Position]`	Trading positions	Key: market_id
`open_bid_orders`	`Dict[str, Dict[str, Order]]`	Open buy orders	`market_id -> {order_hash: Order}`
`open_ask_orders`	`Dict[str, Dict[str, Order]]`	Open sell orders	`market_id -> {order_hash: Order}`
`traded_bid_orders`	`Dict[str, Dict[str, Order]]`	Filled buy orders	`market_id -> {order_hash: Order}`
`traded_ask_orders`	`Dict[str, Dict[str, Order]]`	Filled sell orders	`market_id -> {order_hash: Order}`

Deposit

Property	Type	Description	Note
`denom`	`str`	Token denomination	e.g., “inj”, “peggy0x…”
`total_balance`	`Decimal`	Total deposit amount
`available_balance`	`Decimal`	Available deposit amount	Total minus margins and locked

Position

Property	Type	Description	Note
`subaccount_id`	`str`	Owner subaccount ID
`market_id`	`str`	Market identifier
`quantity`	`Decimal`	Position size
`entry_price`	`Decimal`	Average entry price
`margin`	`Decimal`	Total margin amount
`cumulative_funding_entry`	`Decimal`	Cumulative funding at entry
`is_long`	`bool`	Long (true) or short (false)
`unrealized_pnl`	`Decimal`	Unrealized profit/loss	Default: 0
`total_volume`	`Decimal`	Total trading volume	Default: 0
`trades_count`	`int`	Number of trades	Default: 0
`mark_price`	`Decimal`	Current market price	Optional
`liquidation_price`	`Decimal`	Liquidation threshold	Optional
`margin_ratio`	`Decimal`	Current margin ratio	Optional

Order

Property	Type	Description	Note
`market_id`	`str`	Market identifier
`subaccount_id`	`str`	Subaccount identifier
`order_side`	`Side`	Buy or sell	Side.BUY or Side.SELL
`price`	`Decimal`	Order price
`quantity`	`Decimal`	Order quantity
`order_hash`	`str`	Unique order identifier	Optional, set by chain
`fillable`	`Decimal`	Remaining unfilled quantity	Optional
`filled`	`Decimal`	Filled quantity	Optional
`status`	`OrderStatus`	Order status	BOOKED, PARTIAL_FILLED, etc.
`order_type`	`str`	Order type	Default: “LIMIT”
`margin`	`Decimal`	Margin amount (derivatives)	Optional
`leverage`	`Decimal`	Leverage multiple	Optional
`trigger_price`	`Decimal`	For conditional orders	Optional
`market_type`	`MarketType`	SPOT, DERIVATIVE, BINARY	Optional
`fee_recipient`	`str`	Fee recipient address	Default: ""
`cid`	`str`	Client order ID	Optional
`created_at`	`datetime`	Creation timestamp	Optional
`updated_at`	`datetime`	Last update timestamp	Optional
`position_delta`	`Dict`	Position change data	Optional for derivatives
`payout`	`Decimal`	Expected payout	Optional for derivatives
`tx_hash`	`str`	Transaction hash	Optional
`error_code`	`str`	Error code if failed	Optional
`error_message`	`str`	Error details	Optional

OrderStatus (Enum)

Value	Description
`BOOKED`	Order accepted and active
`PARTIAL_FILLED`	Partially filled
`FILLED`	Completely filled
`CANCELLED`	Cancelled by user
`EXPIRED`	Expired (time-based)

Side (Enum)

Value	Description
`BUY`	Buy order
`SELL`	Sell order

MarketType (Enum)

Value	Description
`SPOT`	Spot market
`DERIVATIVE`	Derivatives market
`BINARY`	Binary options market

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​Strategy Development Guide

​Configuration Guide

​Top-Level Parameters

​Components Section

​Strategies Section

​Trading Mode Configuration

​Direct Execution Mode

​Authorization (Authz) Mode

​RetryConfig Section

​Strategy Development Guide

​Strategy Class Structure

​Strategy Constructor (__init__)

​Initialization Method (on_initialize)

​Strategy Logic (_execute_strategy ) Method

​Best Practices

​Custom Handlers

​Handler Base Class

​Creating a Custom Handler

​Registering Custom Handlers

​Available Handler Types

​Key Data Structure

​Update Types and Corresponding Data Fields

​Strategy Result

​Market

​Orderbook

​Account

​BankBalance

​Balance

​SubAccount

​Deposit

​Position

​Order

​OrderStatus (Enum)

​Side (Enum)

​MarketType (Enum)

Strategy Development Guide

Configuration Guide

Top-Level Parameters

Components Section

Strategies Section

Trading Mode Configuration

Direct Execution Mode

Authorization (Authz) Mode

RetryConfig Section

Strategy Development Guide

Strategy Class Structure

Strategy Constructor (`init`)

Initialization Method (`on_initialize`)

Strategy Logic (`_execute_strategy` ) Method

Best Practices

Custom Handlers

Handler Base Class

Creating a Custom Handler

Registering Custom Handlers

Available Handler Types

Key Data Structure

Update Types and Corresponding Data Fields

Strategy Result

Market

Orderbook

Account

BankBalance

Balance

SubAccount

Deposit

Position

Order

OrderStatus (Enum)

Side (Enum)

MarketType (Enum)