Quant Trading Systems: Architecture & Infrastructure › Key Parameters
Michael BrenndoerferJanuary 21, 20264 min read
Quantitative FinanceAll chapters Part 15 of 15Trading Systems and Production Deployment
Quant Trading Systems: Architecture & Infrastructure › Key ParametersOne section · Sections in this chapter6 / 14 ↓
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The key parameters for the Pre-Trade Risk Checker define the boundaries of acceptable risk:
- max_position_size Maximum notional value allowed for a single position. This limits concentration risk and ensures that no single position can cause catastrophic losses.
- max_order_size Maximum notional value allowed for a single order, preventing accidental large trades. A "fat finger" error that submits an order for 10,000 shares instead of 100 shares is caught by this limit.
- max_concentration Maximum percentage of portfolio value allowed in a single asset. Even if position size limits are respected, concentration limits ensure the portfolio remains diversified.
- max_daily_loss P&L threshold that triggers a suspension of trading. When losses accumulate beyond this level, the system stops trading to prevent further damage and allow human review.
Real-Time Portfolio Risk Monitoring
Beyond individual order checks, the system must continuously monitor aggregate portfolio risk. This includes calculating metrics we covered in Part V, such as Value at Risk and expected shortfall. These metrics capture portfolio-wide risk that individual order checks cannot.
For example, a portfolio might consist entirely of positions that individually pass all risk checks, yet be dangerously exposed to a single risk factor. Real-time monitoring at the portfolio level catches these aggregate risks.
In[20]:
Codefrom scipy import stats
class PortfolioRiskMonitor:
"""Real-time monitoring of portfolio risk metrics."""
def __init__(self, confidence_level: float = 0.95):
self.confidence_level = confidence_level
self.returns_history: list[float] = []
self.peak_value: float = 0
self.current_drawdown: float = 0
def update(self, portfolio_value: float, portfolio_return: float):
"""Update risk metrics with new portfolio state."""
self.returns_history.append(portfolio_return)
# Update peak and drawdown
if portfolio_value > self.peak_value:
self.peak_value = portfolio_value
self.current_drawdown = (
self.peak_value - portfolio_value
) / self.peak_value
def calculate_var(self, horizon_days: int = 1) -> float:
"""Calculate parametric VaR assuming normal returns."""
if len(self.returns_history) < 20:
return float("inf") # Not enough data
returns = np.array(self.returns_history)
mu = returns.mean() * horizon_days
sigma = returns.std() * np.sqrt(horizon_days)
var = -(mu + sigma * stats.norm.ppf(1 - self.confidence_level))
return var
def calculate_expected_shortfall(self, horizon_days: int = 1) -> float:
"""Calculate Expected Shortfall (CVaR)."""
if len(self.returns_history) < 20:
return float("inf")
returns = np.array(self.returns_history)
mu = returns.mean() * horizon_days
sigma = returns.std() * np.sqrt(horizon_days)
alpha = 1 - self.confidence_level
var = -(mu + sigma * stats.norm.ppf(1 - self.confidence_level))
es = -mu + sigma * stats.norm.pdf(stats.norm.ppf(alpha)) / alpha
return es
def get_risk_summary(self) -> dict:
"""Get current risk metric summary."""
return {
"current_drawdown": self.current_drawdown,
"var_1d_95": self.calculate_var(1),
"es_1d_95": self.calculate_expected_shortfall(1),
"realized_vol": np.std(self.returns_history) * np.sqrt(252)
if len(self.returns_history) > 1
else 0,
}In[21]:
Code# Simulate portfolio monitoring
np.random.seed(42)
monitor = PortfolioRiskMonitor(confidence_level=0.95)
# Simulate 60 days of returns
initial_value = 1000000
portfolio_value = initial_value
for day in range(60):
# Simulate daily return
daily_return = np.random.normal(0.0005, 0.015) # ~24% annual vol
portfolio_value *= 1 + daily_return
monitor.update(portfolio_value, daily_return)
risk_summary = monitor.get_risk_summary()Out[22]:
ConsolePortfolio Risk Summary (60-day simulation): --------------------------------------------- Current Drawdown: 16.99% 1-Day VaR (95%): 2.40% 1-Day ES (95%): 2.97% Realized Volatility: 21.45% Final Portfolio Value: $891,555
Out[23]:
Visualization