Designing an AI Hedge Fund: Multi-Agent Architecture for Trading

A single trading agent is fragile. It has one strategy, one set of market assumptions, one signal source. When market conditions rotate — and they always do — the strategy that worked beautifully for six months can start losing money in week seven. This is not a failure of the individual strategy; it is a structural property of all strategies: no single approach works in all regimes.

The solution that institutional hedge funds discovered decades ago is diversification at the strategy level. A fund of five decorrelated strategies — momentum, mean-reversion, arbitrage, DCA, and market-making — will outperform any single one of them over a long enough time horizon, because when one strategy is losing, others are typically winning. AI agents make this architecture more accessible than ever: each strategy can be a separate autonomous agent, coordinated by a lightweight fund manager.

Fund Architecture Overview

An AI hedge fund built on Purple Flea has four types of agents, each with distinct responsibilities:

Fund Manager Agent

The Fund Manager is the capital allocator. It knows the total AUM, the current allocation to each strategy, and each strategy's recent performance. Its primary job is to run an allocation algorithm — Kelly-weighted, equal-weight, or performance-weighted — and update strategy budgets daily or weekly. It also monitors fund-level drawdown and can trigger an orderly wind-down if the fund breaches its maximum drawdown limit.

Strategy Agents

Each strategy agent runs exactly one strategy within a defined capital budget. It receives its allocation from the Fund Manager, executes trades using Purple Flea's trading API, and publishes fill events back to the shared event stream. Keeping strategies isolated as separate agents means you can pause, retrain, or replace one strategy without touching the others.

Risk Agent

The Risk Agent monitors both individual strategies and the fund as a whole. It enforces hard limits: no single strategy can lose more than X% of its allocation in a rolling 7-day window, and the fund's total drawdown from peak cannot exceed Y%. When a limit is breached, the Risk Agent publishes a halt event to the relevant strategy agent. It does not manage capital — only enforces constraints.

Reporting Agent

The Reporting Agent consumes the event stream from all other agents and aggregates performance data. It generates the weekly P&L report, attribution analysis (which strategy contributed what), and risk metrics (Sharpe ratio, max drawdown, Calmar ratio). This agent has no trading permissions and writes only to reporting endpoints.

Capital Allocation: How the Fund Manager Decides

There are three common allocation frameworks for multi-strategy funds:

• Equal weight: Each strategy receives the same capital allocation (1/N). Simple and robust. Appropriate when strategies have similar expected Sharpe ratios and drawdown profiles.
• Performance-weighted: Recent winners receive more capital. Captures momentum in strategy performance but can lead to concentration in strategies that are about to mean-revert.
• Kelly-weighted: Uses each strategy's historical win rate and win/loss ratio to size allocations optimally. Most capital-efficient approach. Requires accurate historical performance data.

Purple Flea's FundClient provides built-in implementations of all three allocation methods, plus custom weighting support:

import purpleflea
from purpleflea.fund import FundClient, AllocationMethod, StrategySpec

# Initialize the fund
fund = FundClient(
    api_key="pf_live_fund_manager_key",
    name="Polaris Fund I",
    total_aum_usd=250000,
    max_fund_drawdown_pct=15,        # halt fund if down 15% from peak
    rebalance_interval_hours=24,     # reallocate capital daily
    allocation_method=AllocationMethod.KELLY_WEIGHTED,
)

# Define strategy agents and their constraints
strategies = [
    StrategySpec(
        agent_id="agent_momentum_001",
        label="momentum",
        api_key="pf_live_momentum_key",
        max_allocation_pct=30,    # never more than 30% of AUM
        min_allocation_pct=5,     # always at least 5% of AUM
        max_drawdown_pct=10,      # halt strategy if down 10% from peak
    ),
    StrategySpec(
        agent_id="agent_meanrev_001",
        label="mean-reversion",
        api_key="pf_live_meanrev_key",
        max_allocation_pct=25,
        min_allocation_pct=5,
        max_drawdown_pct=8,
    ),
    StrategySpec(
        agent_id="agent_arb_001",
        label="arbitrage",
        api_key="pf_live_arb_key",
        max_allocation_pct=40,    # arb benefits from more capital
        min_allocation_pct=10,
        max_drawdown_pct=5,       # arb should have tight drawdown limits
    ),
    StrategySpec(
        agent_id="agent_dca_001",
        label="dca",
        api_key="pf_live_dca_key",
        max_allocation_pct=20,
        min_allocation_pct=5,
        max_drawdown_pct=20,      # DCA tolerates higher drawdown by design
    ),
]

# Launch the fund — this starts all strategy agents
fund.launch(strategies=strategies)
print(f"Fund launched with {len(strategies)} strategies")
print(f"Initial allocations: {fund.get_allocations()}")

Inter-Agent Communication via Events

The agents in a fund need to coordinate without tight coupling. Strategy agents should not need to know about each other or directly call the Fund Manager. Purple Flea's event system provides a pub/sub layer that keeps agents loosely coupled:

• Strategy agents publish fill, position_opened, position_closed, and drawdown_alert events.
• The Risk Agent subscribes to all agent events and publishes halt_strategy and resume_strategy events.
• The Fund Manager subscribes to fill and P&L events and publishes rebalance events when allocations change.
• The Reporting Agent subscribes to all events and writes them to the reporting database.

This architecture means you can add a new strategy agent to the fund without changing any existing agent's code — just subscribe it to the appropriate event channels.

Risk Management at Fund Level

The Risk Agent's job is to enforce fund-wide constraints that individual strategy agents should not be expected to track themselves. Here is how to set up the Risk Agent with correlation monitoring — one of the most important and often-overlooked fund-level risk controls:

import purpleflea
from purpleflea.fund import RiskAgent, RiskConfig, CorrelationLimit

# Initialize the risk agent (read-only trading access, write to halt system)
risk = RiskAgent(
    api_key="pf_live_risk_agent_key",
    fund_id="fund_polaris_001",
    config=RiskConfig(
        # Fund-wide hard limits
        max_fund_drawdown_pct=15,
        max_daily_loss_usd=5000,
        max_open_positions=20,        # across all strategies combined
        # Per-strategy limits are set in StrategySpec above
        # Correlation monitoring
        correlation_limits=[
            CorrelationLimit(
                strategy_a="momentum",
                strategy_b="mean-reversion",
                max_correlation=0.6,        # halt if strategies become too correlated
                lookback_hours=168          # 7-day rolling window
            ),
        ],
        # Notification targets
        alert_webhook="https://hooks.slack.com/your-webhook",
        escalate_to_human_on_halt=True,
    )
)

# Start the risk agent — it runs continuously in the background
risk.start()
print("Risk agent monitoring started")

# You can also query risk metrics manually
status = risk.get_fund_risk_status()
print(f"Fund drawdown from peak: {status.drawdown_from_peak_pct:.1f}%")
print(f"Active strategies: {status.active_strategy_count}")
print(f"Halted strategies: {[s.label for s in status.halted_strategies]}")
print(f"Worst correlation pair: {status.max_correlation_pair} @ {status.max_correlation:.2f}")

Performance Attribution

Understanding which strategy is driving the fund's returns — and which is dragging — is essential for allocation decisions and for deciding whether to retire a strategy. Performance attribution answers: of the fund's X% return this month, how much came from momentum, how much from arbitrage, and how much from DCA?

Purple Flea's fund analytics API computes attribution automatically using the allocation-weighted return methodology. Each strategy's return is weighted by its average capital allocation over the period to compute its contribution to total fund return. This correctly accounts for the fact that a strategy with 5% allocation contributes proportionally less to fund return than one with 30%, even if it has a higher individual return rate.

Growing the Fund: Reinvestment, Scaling, and New Capital

A well-run multi-agent fund creates a natural growth flywheel:

• Reinvesting profits: Configure the Fund Manager to automatically reinvest net profits each week, increasing the total AUM available to strategies. Compounding at 5%/month doubles AUM in about 14 months.
• Scaling position sizes: As AUM grows, strategy position sizes can grow proportionally. Purple Flea's trading API accepts position sizes in both absolute dollar terms and as a percentage of strategy allocation — use the latter to auto-scale.
• On-ramping new capital: Use Purple Flea's escrow API to accept capital from external contributors with defined terms — profit splits, lockup periods, and redemption windows — without requiring trust in your counterparty.

Referral Income as Passive Fund Revenue

One structural advantage of running a multi-agent fund on Purple Flea is the referral income generated by your strategy agents. When you create each strategy agent using your referral link, you earn 20% of all trading fees that agent generates, passively, in perpetuity. A fund with four strategy agents collectively generating $100,000/month in trading volume at 0.05% taker fee = $50/month in passive referral income per agent, $200/month total, without any additional work.

Conclusion

The multi-agent hedge fund architecture is the natural evolutionary endpoint for serious AI trading deployments. Single-strategy bots are a starting point. A coordinated fund with a dedicated risk agent, performance attribution, and automated capital reallocation is a production-grade system. Purple Flea's FundClient, event system, and risk primitives provide the infrastructure layer so you can focus on the strategies themselves — the actual source of alpha.

Start small: a two-strategy fund with equal-weight allocation and a simple Risk Agent enforcing drawdown limits. Measure performance, add a third strategy, implement Kelly weighting when you have 90 days of clean data. The architecture is designed to grow with you.