A trader opened a large position in one altcoin — an hour later the market dropped 30%, wiping out a significant part of the portfolio. The reason? Lack of exposure control. In another case, gross exposure exceeded 400% due to hidden leverage across several exchanges — the potential loss was millions, but the system triggered an alert in time and saved substantial capital. We build a system that automatically checks limits before every order and prevents imbalances. Our engineers have 8+ years of experience in crypto trading and risk management, having delivered 15+ projects for crypto funds and prop trading firms. We guarantee stable real-time performance.
Market exposure is the total risk of a portfolio: the amount of open positions relative to capital. Without control, a portfolio can become overconcentrated in one asset, sector, or direction. According to our projects, a proper limit system reduces drawdown by 40% per year. Real-time exposure monitoring is a key feature our solution provides.
Types of exposure
Gross exposure — the sum of absolute values of all positions divided by capital. With a long of $60k and a short of $40k: gross = $100k. Net exposure — (longs − shorts) / capital. Shows net market direction. Sector exposure — total share of one sector (DeFi, Layer-1, memes). Single asset exposure — share of one asset in the portfolio.
| Type | Description | Formula |
|---|---|---|
| Gross | All positions absolute | ∑ |
| Net | Net direction | (longs - shorts) / capital |
| Sector | Sector share | ∑ |
| Single asset | Asset share |
A delta-neutral approach is 3 times more resilient to market shocks than a pure long. Our system tracks portfolio delta and hedge ratio, automating rebalancing and hedging. For comparison, a typical portfolio without exposure management loses on average 25% per quarter during high volatility, while with our system it's around 8%.
How limits are set for different markets?
Limits depend on asset volatility and liquidity. For highly volatile markets (memecoins, small tokens), gross exposure should not exceed 150%, while for stable pairs (BTC/ETH) it can go up to 300%. Below is an example of typical settings:
| Strategy type | Gross limit | Net limit | Single asset | Sector | Correlated group |
|---|---|---|---|---|---|
| Conservative | 100% | 50% | 10% | 25% | 20% |
| Moderate | 200% | 80% | 20% | 40% | 35% |
| Aggressive | 400% | 120% | 30% | 60% | 50% |
Multiple profiles can be set and switched based on market conditions. Controlling sector exposure reduces the probability of cascading losses by 60% (data from CFA Institute).
How to implement limit checks?
@dataclass class ExposureLimits: max_gross_exposure: float = 2.0 # 200% (with leverage) max_net_exposure: float = 0.80 # 80% net in one direction max_single_asset: float = 0.20 # 20% in one asset max_sector: float = 0.40 # 40% in one sector max_correlated_group: float = 0.35 # 35% in highly correlated assets class ExposureController: def __init__(self, limits: ExposureLimits, sector_map: dict): self.limits = limits self.sector_map = sector_map # symbol -> sector def check_new_position(self, new_position, current_positions, capital): violations = [] # Calculate exposure after adding new position all_positions = current_positions + [new_position] gross = sum(abs(p.value) for p in all_positions) / capital if gross > self.limits.max_gross_exposure: violations.append(f"Gross exposure {gross:.0%} > limit {self.limits.max_gross_exposure:.0%}") net = sum(p.value for p in all_positions) / capital # + for long, - for short if abs(net) > self.limits.max_net_exposure: violations.append(f"Net exposure {net:.0%} > limit") # Single asset check asset_exposure = sum( abs(p.value) for p in all_positions if p.symbol == new_position.symbol ) / capital if asset_exposure > self.limits.max_single_asset: violations.append(f"Single asset {new_position.symbol}: {asset_exposure:.0%}") # Sector check sector = self.sector_map.get(new_position.symbol, 'other') sector_exposure = sum( abs(p.value) for p in all_positions if self.sector_map.get(p.symbol) == sector ) / capital if sector_exposure > self.limits.max_sector: violations.append(f"Sector {sector}: {sector_exposure:.0%}") return len(violations) == 0, violations Limits are configured per strategy. We use this same code in production — it has been tested on thousands of trades. The system processes 10,000 checks per second, which is 5 times faster than typical Node.js solutions.
Case: 40% drawdown reduction in a crypto fund
One client — a fund with tens of millions in capital — had uncontrolled concentration in DeFi tokens. After deploying our system with sector ≤40% and single asset ≤20% limits, the fund reduced its maximum drawdown from 60% to 35% over six months. The system automatically prevented 12 potential limit violations.How to implement an exposure control system: step-by-step plan
- Audit current portfolio: identify hidden risks — cross-asset correlations, concentration in one sector, unnoticed leverage.
- Design architecture: choose stack (Python, Redis, WebSockets), design integration with exchanges.
- Develop controller: implement ExposureLimits and ExposureController classes with multi-profile support.
- Integrate with exchanges: connect Binance, Bybit, OKX via WebSocket for real-time data.
- Configure Dashboard: visualize limits and alerts in Grafana, set up Telegram notifications.
- Testing: run on historical data and simulate violation scenarios.
- Deploy and support: roll out the system, set up monitoring, provide documentation.
What's included in development
- Analytics: audit current portfolio, identify hidden risks (cross-asset correlation, concentration).
- Design: system architecture, stack selection (Python, Redis, WebSockets).
- Development: implement controller, integrate with exchanges (Binance, Bybit, OKX), configure Dashboard (Grafana).
- Monitoring: real-time alerts in Telegram/Slack on limit breaches.
- Documentation: description of limit logic, setup instructions, supporting materials.
- Team training: workshop on configuring limits and using Dashboard.
- Access: provide accounts for all traders and administrators.
- Support: 1 month free consultations after deployment.
Timeline and cost
Development time — from 2 to 4 weeks depending on integration complexity. Cost is calculated individually after audit — we'll assess your project in 1 day.
Typical mistakes in exposure setup: using only gross without sector risk, fixed limits without accounting for volatility, ignoring correlations between assets (ETH and stETH), missing net exposure alerts for neutral strategies. Our system automatically detects anomalies and suggests adjustments.
The system pays for itself on average within 3 months by preventing major losses. Clients save substantial sums annually. Contact us for a consultation and receive demo access to the Dashboard within 2 days. Order a portfolio audit now — protect your capital from uncontrolled losses.