📦 SQA::RiskManager¶

Description

RiskManager - Comprehensive risk management and position sizing

Provides methods for: - Value at Risk (VaR): Historical, Parametric, Monte Carlo - Conditional VaR (CVaR / Expected Shortfall) - Position sizing: Kelly Criterion, Fixed Fractional, Percent Volatility - Risk metrics: Sharpe, Sortino, Calmar, Maximum Drawdown - Stop loss calculations

Source Information

Defined in: lib/sqa/risk_manager.rb:29

Inherits from: Object

🏭 Class Methods¶

`.var(returns, confidence: = 0.95, method: = :historical, simulations: = 10_000)`¶

Calculate Value at Risk (VaR) using historical method

VaR represents the maximum expected loss over a given time period at a specified confidence level.

Parameters

Name	Type	Description
`returns`	`Array<Float>`	Array of period returns (e.g., daily returns)
`confidence`	`Float`	Confidence level (default: 0.95 for 95%)
`method`	`Symbol`	Method to use (:historical, :parametric, :monte_carlo)
`simulations`	`Integer`	Number of Monte Carlo simulations (if method is :monte_carlo)

Returns

Type: Float

Value at Risk as a percentage

Usage Examples

returns = [0.01, -0.02, 0.015, -0.01, 0.005]
var = SQA::RiskManager.var(returns, confidence: 0.95)
# => -0.02 (2% maximum expected loss at 95% confidence)

Source Location

lib/sqa/risk_manager.rb:48

`.cvar(returns, confidence: = 0.95)`¶

Calculate Conditional Value at Risk (CVaR / Expected Shortfall)

CVaR is the expected loss given that the loss exceeds the VaR threshold. It provides a more conservative risk measure than VaR.

Parameters

Name	Type	Description
`returns`	`Array<Float>`	Array of period returns
`confidence`	`Float`	Confidence level (default: 0.95)

Returns

Type: Float

CVaR as a percentage

Usage Examples

cvar = SQA::RiskManager.cvar(returns, confidence: 0.95)
# => -0.025 (2.5% expected loss in worst 5% of cases)

Source Location

lib/sqa/risk_manager.rb:77

`.kelly_criterion(win_rate:, avg_win:, avg_loss:, capital:, max_fraction: = 0.25)`¶

Calculate position size using Kelly Criterion

Kelly Criterion calculates the optimal fraction of capital to risk based on win rate and win/loss ratio.

Formula: f = (p * b - q) / b where: f = fraction of capital to bet p = probability of winning q = probability of losing (1 - p) b = win/loss ratio (avg_win / avg_loss)

Parameters

Name	Type	Description
`win_rate`	`Float`	Win rate (0.0 to 1.0)
`avg_win`	`Float`	Average win size (as percentage)
`avg_loss`	`Float`	Average loss size (as percentage)
`capital`	`Float`	Total capital available
`max_fraction`	`Float`	Maximum fraction to risk (default: 0.25 for 25%)

Returns

Type: Float

Dollar amount to risk

Usage Examples

position = SQA::RiskManager.kelly_criterion(
  win_rate: 0.60,
  avg_win: 0.10,
  avg_loss: 0.05,
  capital: 10_000,
  max_fraction: 0.25
)

Source Location

lib/sqa/risk_manager.rb:116

`.fixed_fractional(capital:, risk_fraction: = 0.02)`¶

Calculate position size using Fixed Fractional method

Risk a fixed percentage of capital on each trade. Simple and conservative approach.

Parameters

Name	Type	Description
`capital`	`Float`	Total capital
`risk_fraction`	`Float`	Fraction to risk (e.g., 0.02 for 2%)

Returns

Type: Float

Dollar amount to risk

Usage Examples

position = SQA::RiskManager.fixed_fractional(capital: 10_000, risk_fraction: 0.02)
# => 200.0 (risk $200 per trade)

Source Location

lib/sqa/risk_manager.rb:146

`.percent_volatility(capital:, returns:, target_volatility: = 0.15, current_price:)`¶

Calculate position size using Percent Volatility method

Adjust position size based on recent volatility. Higher volatility = smaller position size.

Parameters

Name	Type	Description
`capital`	`Float`	Total capital
`returns`	`Array<Float>`	Recent returns
`target_volatility`	`Float`	Target portfolio volatility (annualized)
`current_price`	`Float`	Current asset price

Returns

Type: Integer

Number of shares to buy

Usage Examples

shares = SQA::RiskManager.percent_volatility(
  capital: 10_000,
  returns: recent_returns,
  target_volatility: 0.15,
  current_price: 150.0
)

Source Location

lib/sqa/risk_manager.rb:170

`.atr_stop_loss(current_price:, atr:, multiplier: = 2.0, direction: = :long)`¶

Calculate stop loss price based on ATR (Average True Range)

Parameters

Name	Type	Description
`current_price`	`Float`	Current asset price
`atr`	`Float`	Average True Range
`multiplier`	`Float`	ATR multiplier (default: 2.0)
`direction`	`Symbol`	:long or :short

Returns

Type: Float

Stop loss price

Usage Examples

stop = SQA::RiskManager.atr_stop_loss(
  current_price: 150.0,
  atr: 3.5,
  multiplier: 2.0,
  direction: :long
)
# => 143.0 (stop at current - 2*ATR)

Source Location

lib/sqa/risk_manager.rb:204

`.max_drawdown(prices)`¶

Calculate maximum drawdown from price series

Drawdown is the peak-to-trough decline in portfolio value.

Parameters

Name	Type	Description
`prices`	`Array<Float>`	Array of prices or portfolio values

Returns

Type: Hash

{ max_drawdown: Float, peak_idx: Integer, trough_idx: Integer }

Usage Examples

dd = SQA::RiskManager.max_drawdown([100, 110, 105, 95, 100])
# => { max_drawdown: -0.136, peak_idx: 1, trough_idx: 3 }

Source Location

lib/sqa/risk_manager.rb:224

`.sharpe_ratio(returns, risk_free_rate: = 0.02, periods_per_year: = 252)`¶

Calculate Sharpe Ratio

Measures risk-adjusted return (excess return per unit of risk).

Parameters

Name	Type	Description
`returns`	`Array<Float>`	Array of period returns
`risk_free_rate`	`Float`	Risk-free rate (annualized, default: 0.02)
`periods_per_year`	`Integer`	Number of periods per year (default: 252 for daily)

Returns

Type: Float

Sharpe ratio

Usage Examples

sharpe = SQA::RiskManager.sharpe_ratio(returns, risk_free_rate: 0.02)

Source Location

lib/sqa/risk_manager.rb:269

`.sortino_ratio(returns, target_return: = 0.0, periods_per_year: = 252)`¶

Calculate Sortino Ratio

Like Sharpe ratio but only penalizes downside volatility.

Parameters

Name	Type	Description
`returns`	`Array<Float>`	Array of period returns
`target_return`	`Float`	Target return (default: 0.0)
`periods_per_year`	`Integer`	Number of periods per year (default: 252)

Returns

Type: Float

Sortino ratio

Usage Examples

sortino = SQA::RiskManager.sortino_ratio(returns)

Source Location

lib/sqa/risk_manager.rb:294

`.calmar_ratio(returns, periods_per_year: = 252)`¶

Calculate Calmar Ratio

Ratio of annualized return to maximum drawdown.

Parameters

Name	Type	Description
`returns`	`Array<Float>`	Array of period returns
`periods_per_year`	`Integer`	Number of periods per year (default: 252)

Returns

Type: Float

Calmar ratio

Usage Examples

calmar = SQA::RiskManager.calmar_ratio(returns)

Source Location

lib/sqa/risk_manager.rb:325

`.monte_carlo_simulation(initial_capital:, returns:, periods:, simulations: = 1000)`¶

Monte Carlo simulation for portfolio value

Parameters

Name	Type	Description
`initial_capital`	`Float`	Starting capital
`returns`	`Array<Float>`	Historical returns to sample from
`periods`	`Integer`	Number of periods to simulate
`simulations`	`Integer`	Number of simulation paths

Returns

Type: Hash

Simulation results with percentiles

Usage Examples

results = SQA::RiskManager.monte_carlo_simulation(
  initial_capital: 10_000,
  returns: historical_returns,
  periods: 252,
  simulations: 1000
)
puts "95th percentile: $#{results[:percentile_95]}"

Source Location

lib/sqa/risk_manager.rb:360

📦 SQA::RiskManager¶

🏭 Class Methods¶

.var(returns, confidence: = 0.95, method: = :historical, simulations: = 10_000)¶

.cvar(returns, confidence: = 0.95)¶

.kelly_criterion(win_rate:, avg_win:, avg_loss:, capital:, max_fraction: = 0.25)¶

.fixed_fractional(capital:, risk_fraction: = 0.02)¶

.percent_volatility(capital:, returns:, target_volatility: = 0.15, current_price:)¶

.atr_stop_loss(current_price:, atr:, multiplier: = 2.0, direction: = :long)¶

.max_drawdown(prices)¶

.sharpe_ratio(returns, risk_free_rate: = 0.02, periods_per_year: = 252)¶

.sortino_ratio(returns, target_return: = 0.0, periods_per_year: = 252)¶

.calmar_ratio(returns, periods_per_year: = 252)¶

.monte_carlo_simulation(initial_capital:, returns:, periods:, simulations: = 1000)¶

📝 Attributes¶

`.var(returns, confidence: = 0.95, method: = :historical, simulations: = 10_000)`¶

`.cvar(returns, confidence: = 0.95)`¶

`.kelly_criterion(win_rate:, avg_win:, avg_loss:, capital:, max_fraction: = 0.25)`¶

`.fixed_fractional(capital:, risk_fraction: = 0.02)`¶

`.percent_volatility(capital:, returns:, target_volatility: = 0.15, current_price:)`¶

`.atr_stop_loss(current_price:, atr:, multiplier: = 2.0, direction: = :long)`¶

`.max_drawdown(prices)`¶

`.sharpe_ratio(returns, risk_free_rate: = 0.02, periods_per_year: = 252)`¶

`.sortino_ratio(returns, target_return: = 0.0, periods_per_year: = 252)`¶

`.calmar_ratio(returns, periods_per_year: = 252)`¶

`.monte_carlo_simulation(initial_capital:, returns:, periods:, simulations: = 1000)`¶