Leverage Trading API for AI Agents

Why Leverage Matters for AI Agents

Leverage multiplies both gains and losses. AI agents, unlike humans, can apply risk management rules with perfect consistency — making leverage safer in algorithmic hands.

📈

Capital Efficiency

With 10x leverage, $100 controls $1,000 of position. An agent can deploy its full capital across more positions simultaneously, diversifying risk while amplifying potential returns.

🎯

Precise Risk Sizing

Agents can target exact dollar-risk per trade regardless of leverage. A $10 risk on a $1,000 position is a 1% stop-loss with 1x leverage, or a 0.1% stop-loss with 10x. Precision impossible for humans.

🔄

Delta-Neutral Strategies

Low leverage (1-3x) enables delta-neutral funding rate harvesting — earning the funding payment without directional risk. A reliable 30-150% APY strategy that leverage makes efficient.

⚡

Instant Execution

Leveraged positions respond instantly to agent signals. When your LLM detects a market opportunity, Purple Flea executes the leveraged trade in under 100ms — before the opportunity closes.

🛡️

Programmatic Stop-Loss

Every leveraged position gets an automatic stop-loss. Agents never forget to set one (unlike humans). Set stop-loss and take-profit levels at trade opening, adjust via API as market moves.

📊

Real-Time P&L Tracking

Every position returns live P&L, liquidation price, margin ratio, and funding accrued. Agents monitor portfolio health and auto-deleverage before reaching dangerous margin levels.

Leverage Trading Code Examples

Open Leveraged Position

import purpleflea as pf

client = pf.Client(api_key="YOUR_KEY")

# Open 5x leveraged long on BTC
trade = client.trading.open_trade(
    symbol="BTC",
    side="long",
    size=0.01,       # 0.01 BTC
    leverage=5,     # 5x leverage
    order_type="market",
    stop_loss=0.02,  # 2% stop-loss
    take_profit=0.06 # 6% take-profit (3:1 RR)
)

print(f"Position: {trade['position_id']}")
print(f"Fill price: ${trade['fill_price']:,.2f}")
print(f"Liquidation price: ${trade['liquidation_price']:,.2f}")
print(f"Required margin: ${trade['margin_required']:.2f}")

# Monitor position
pos = client.trading.get_position(trade['position_id'])
print(f"Unrealized P&L: ${pos['unrealized_pnl']:.2f}")
print(f"Current leverage: {pos['effective_leverage']:.1f}x")
          

Risk-Managed Agent

class LeverageRiskManager:
    """Enforces leverage limits and position sizing."""

    MAX_LEVERAGE = 5
    MAX_PORTFOLIO_RISK = 0.02  # 2% per trade
    MAX_TOTAL_LEVERAGE = 2.0   # Portfolio-level

    def __init__(self, client):
        self.client = client

    def safe_open_trade(self, symbol: str, direction: str,
                           confidence: float) -> dict | None:
        # Get current portfolio state
        balance = self.client.wallet.get_balance()
        positions = self.client.trading.get_all_positions()
        total_exposure = sum(p['notional_value'] for p in positions)
        portfolio_value = float(balance['total_usd'])

        current_leverage = total_exposure / portfolio_value
        if current_leverage >= self.MAX_TOTAL_LEVERAGE:
            print(f"Portfolio leverage {current_leverage:.1f}x at max. Skipping.")
            return None

        # Size based on confidence and risk limit
        risk_usd = portfolio_value * self.MAX_PORTFOLIO_RISK * confidence
        leverage = min(int(confidence * 10), self.MAX_LEVERAGE)

        price = self.client.trading.get_price(symbol)['price']
        size = (risk_usd * leverage) / price / 0.02  # 2% stop

        trade = self.client.trading.open_trade(
            symbol=symbol,
            side=direction,
            size=size,
            leverage=leverage,
            stop_loss=0.02,
            take_profit=0.06
        )
        print(f"Opened {direction} {symbol} {leverage}x | risk: ${risk_usd:.2f}")
        return trade
          

Why Leverage Matters for AI Agents

Leverage Trading Code Examples

Open Leveraged Position

Risk-Managed Agent

Give Your Agents Leverage