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feat(trading_service): wire risk gRPC to real RiskEngine and kill switch

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- emergency_stop: delegates to TradingServiceKillSwitch.emergency_shutdown()
  which activates the global AtomicKillSwitch; returns Status::unavailable
  when kill_switch_system is None rather than silently succeeding
- validate_order: reads max_order_quantity from config repository (falls back
  to 1_000_000); additionally calls RiskEngine.check_var_limit() for VaR
  validation when symbol and price are provided
- get_va_r: uses RiskEngine.calculate_marginal_var() for real VaR with a
  parametric fallback; per-symbol marginal VaRs computed individually
- get_risk_metrics: derives portfolio_var_1d from RiskEngine; scales to 5d
  and 30d via sqrt-of-time rule; remaining fields (Sharpe, beta, alpha,
  current_drawdown) keep placeholder values with explicit TODO comments

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
jgrusewski
2026-02-21 23:44:43 +01:00
parent 8855177f92
commit 8ae076434d
2 changed files with 264 additions and 94 deletions
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144
services/trading_service/src/services/ml.rs
View File

@@ -2,10 +2,13 @@
use crate::proto::ml::{
ml_service_server::MlService, GetModelStatusRequest, GetModelStatusResponse,
GetPredictionRequest, GetPredictionResponse, RetrainModelRequest, RetrainModelResponse,
GetPredictionRequest, GetPredictionResponse, ModelHealth, ModelState, ModelStatus, Prediction,
PredictionType, RetrainModelRequest, RetrainModelResponse,
};
use crate::state::TradingServiceState;
use std::sync::Arc;
use crate::state::{TradingActionType, TradingServiceState};
use ml::ensemble::TradingAction;
use std::collections::HashMap;
use std::time::{SystemTime, UNIX_EPOCH};
use tonic::{Request, Response, Status};
/// ML service implementation
@@ -21,6 +24,15 @@ impl MLServiceImpl {
}
}
/// Map a `TradingActionType` to the proto `PredictionType` integer.
fn action_to_prediction_type(action: TradingActionType) -> i32 {
match action {
TradingActionType::Buy => PredictionType::Buy as i32,
TradingActionType::Sell => PredictionType::Sell as i32,
TradingActionType::Hold => PredictionType::Hold as i32,
}
}
#[tonic::async_trait]
impl MlService for MLServiceImpl {
async fn get_prediction(
@@ -29,38 +41,47 @@ impl MlService for MLServiceImpl {
) -> Result<Response<GetPredictionResponse>, Status> {
let req = request.into_inner();
// Get ML inference timeout from repository
let timeout = self
// Return Unavailable when no ensemble coordinator is loaded so callers
// know there is no real ML inference available rather than receiving
// silently incorrect hard-coded values.
if self.state.ensemble_coordinator.is_none() {
return Err(Status::unavailable(
"Ensemble coordinator is not initialised — no ML models loaded",
));
}
// Delegate to the full ensemble prediction flow:
// feature extraction -> ensemble inference -> risk-based position sizing.
let signal = self
.state
.config_repository
.get_config_u64("MachineLearning", "inference_timeout_ms")
.get_ensemble_trading_signal(&req.symbol)
.await
.map_err(|e| Status::internal(format!("Failed to get ML inference timeout: {}", e)))?
.unwrap_or(100);
.map_err(|e| Status::internal(format!("Ensemble prediction failed: {}", e)))?;
// Placeholder prediction logic
let prediction_value = match req.model_name.as_str() {
"price_prediction" => 0.001, // Simple price movement
"volatility_prediction" => 0.02, // 2% volatility
"liquidity_prediction" => 0.8, // 80% liquidity score
_ => 0.0,
};
let prediction_type = action_to_prediction_type(signal.action);
let prediction = crate::proto::ml::Prediction {
model_name: req.model_name,
symbol: req.symbol,
prediction_type: 1, // Assuming 1 = BUY
value: prediction_value,
confidence: 0.85,
let now_ns = SystemTime::now()
.duration_since(UNIX_EPOCH)
.unwrap_or_default()
.as_nanos() as i64;
let prediction = Prediction {
model_name: req.model_name.clone(),
symbol: req.symbol.clone(),
prediction_type,
// Express the ensemble signal as confidence weighted by agreement.
value: signal.confidence * (1.0 - signal.disagreement_rate),
confidence: signal.confidence,
horizon_minutes: req.horizon_minutes.unwrap_or(30),
features: vec![], // Empty for now
timestamp: chrono::Utc::now().timestamp(),
// Feature population is handled separately by Task 11.
features: vec![],
timestamp: now_ns,
};
Ok(Response::new(GetPredictionResponse {
prediction: Some(prediction),
confidence: 0.85,
timestamp: chrono::Utc::now().timestamp(),
confidence: signal.confidence,
timestamp: now_ns,
}))
}
@@ -68,25 +89,60 @@ impl MlService for MLServiceImpl {
&self,
_request: Request<GetModelStatusRequest>,
) -> Result<Response<GetModelStatusResponse>, Status> {
// Get ML model settings from repository
let inference_timeout = self
.state
.config_repository
.get_config_u64("MachineLearning", "inference_timeout_ms")
.await
.map_err(|e| Status::internal(format!("Failed to get ML inference timeout: {}", e)))?
.unwrap_or(100);
let now_ts = SystemTime::now()
.duration_since(UNIX_EPOCH)
.unwrap_or_default()
.as_secs() as i64;
Ok(Response::new(GetModelStatusResponse {
models_loaded: vec![
"price_prediction".to_string(),
"volatility_prediction".to_string(),
"liquidity_prediction".to_string(),
],
total_models: 3,
inference_timeout_ms: inference_timeout,
gpu_enabled: cfg!(feature = "gpu"),
}))
// Build one `ModelStatus` per registered model when the ensemble
// coordinator is available; fall back to a single offline entry
// when no coordinator has been loaded.
let model_statuses = match &self.state.ensemble_coordinator {
Some(coordinator) => {
let model_count = coordinator.model_count().await;
// The coordinator registers models by name; these are the four
// production models (DQN, PPO, TFT, MAMBA-2).
let default_names = ["DQN", "PPO", "TFT", "MAMBA-2"];
let mut statuses = Vec::with_capacity(model_count);
for i in 0..model_count {
let name = default_names
.get(i)
.map(|s| s.to_string())
.unwrap_or_else(|| format!("model_{}", i));
statuses.push(ModelStatus {
model_name: name,
state: ModelState::Ready as i32,
error_message: None,
last_updated: now_ts,
last_prediction: now_ts,
health: ModelHealth::Healthy as i32,
metadata: HashMap::new(),
});
}
statuses
},
None => {
// No ensemble loaded — report a single offline entry so the
// caller knows the service is running but models are absent.
vec![ModelStatus {
model_name: "ensemble".to_string(),
state: ModelState::Offline as i32,
error_message: Some(
"Ensemble coordinator not initialised".to_string(),
),
last_updated: now_ts,
last_prediction: 0,
health: ModelHealth::Unhealthy as i32,
metadata: HashMap::new(),
}]
},
};
Ok(Response::new(GetModelStatusResponse { model_statuses }))
}
// update_model_config method removed - not in proto definition

214
services/trading_service/src/services/risk.rs
View File

@@ -11,6 +11,7 @@ use crate::proto::risk::{
use crate::repositories::ConfigRepository;
use crate::state::TradingServiceState;
use tonic::{Request, Response, Status};
use tracing::{error, info, warn};
/// Risk service implementation
#[derive(Debug, Clone)]
@@ -33,25 +34,69 @@ impl RiskService for RiskServiceImpl {
) -> Result<Response<GetVaRResponse>, Status> {
let req = request.into_inner();
// Get VaR confidence from repository
let confidence_level = req.confidence_level;
let lookback_days = req.lookback_days;
let method = VaRMethod::try_from(req.method).unwrap_or(VaRMethod::VarMethodHistorical);
// Placeholder VaR calculation
let portfolio_var = confidence_level * 0.02; // 2% volatility assumption
// Delegate to the real RiskEngine for marginal VaR.
// calculate_comprehensive_var requires full historical price data that is not available
// at the gRPC boundary, so we use calculate_marginal_var as the portfolio-level estimate
// with a representative notional value.
// TODO: Feed real position data from position_manager when available.
let risk_engine = self.state.risk_engine.read().await;
let portfolio_var = match risk_engine
.calculate_marginal_var(
"portfolio",
"PORTFOLIO",
confidence_level * 1_000_000.0, // notional proxy scaled by confidence
1.0,
)
.await
{
Ok(var) => {
info!("VaR calculated via RiskEngine: {:.4}", var);
var
},
Err(e) => {
warn!(
"RiskEngine VaR calculation failed ({}), falling back to parametric estimate",
e
);
// Parametric fallback: confidence_level * 2% daily volatility assumption
confidence_level * 0.02
},
};
// Create symbol VaRs from requested symbols
let symbol_vars = req
.symbols
.into_iter()
.map(|symbol| SymbolVaR {
symbol: symbol,
var_value: portfolio_var * 0.1, // Assume each symbol contributes 10%
position_size: 1000.0, // Placeholder position size
contribution_pct: 10.0, // Placeholder contribution percentage
})
.collect();
// Build per-symbol marginal VaRs using the same engine.
// Each symbol's contribution is calculated individually; if a symbol fails we skip it.
let num_symbols = req.symbols.len();
let equal_contribution_pct = if num_symbols > 0 {
100.0 / num_symbols as f64
} else {
0.0
};
let mut symbol_vars = Vec::with_capacity(num_symbols);
for symbol in req.symbols {
let symbol_var = match risk_engine
.calculate_marginal_var("portfolio", &symbol, 1000.0, 1.0)
.await
{
Ok(var) => var,
Err(e) => {
warn!("Marginal VaR for symbol {} failed: {}", symbol, e);
// Fall back to equal-weight share of portfolio VaR
portfolio_var * (equal_contribution_pct / 100.0)
},
};
symbol_vars.push(SymbolVaR {
symbol,
var_value: symbol_var,
position_size: 1000.0, // TODO: read from position_manager
contribution_pct: equal_contribution_pct, // TODO: compute actual marginal contribution
});
}
Ok(Response::new(GetVaRResponse {
portfolio_var,
@@ -67,37 +112,38 @@ impl RiskService for RiskServiceImpl {
&self,
_request: Request<GetRiskMetricsRequest>,
) -> Result<Response<GetRiskMetricsResponse>, Status> {
// Create comprehensive risk metrics from repository data
let portfolio_var_1d = self
.state
.config_repository
.get_config_f64("Risk", "portfolio_var_1d")
.await
.map_err(|e| Status::internal(format!("Failed to get 1d VaR: {}", e)))?
.unwrap_or(0.02);
// Use the real RiskEngine's configured confidence level and max VaR limit
// to produce the 1d VaR estimate. Longer horizons scale by sqrt(T).
let risk_engine = self.state.risk_engine.read().await;
let confidence = risk_engine.var_confidence();
let portfolio_var_5d = self
.state
.config_repository
.get_config_f64("Risk", "portfolio_var_5d")
// Compute a representative 1-day portfolio VaR via marginal VaR.
// TODO: Replace with calculate_comprehensive_var once position_manager
// provides real PositionInfo and historical price data.
let portfolio_var_1d = match risk_engine
.calculate_marginal_var("portfolio", "PORTFOLIO", confidence * 1_000_000.0, 1.0)
.await
.map_err(|e| Status::internal(format!("Failed to get 5d VaR: {}", e)))?
.unwrap_or(0.05);
{
Ok(var) => var,
Err(e) => {
warn!("RiskEngine marginal VaR failed for get_risk_metrics: {}", e);
// Parametric fallback using configured confidence level
confidence * 0.02
},
};
let portfolio_var_30d = self
.state
.config_repository
.get_config_f64("Risk", "portfolio_var_30d")
.await
.map_err(|e| Status::internal(format!("Failed to get 30d VaR: {}", e)))?
.unwrap_or(0.15);
// Scale 1d VaR to 5d and 30d using square-root-of-time rule
let portfolio_var_5d = portfolio_var_1d * 5_f64.sqrt();
let portfolio_var_30d = portfolio_var_1d * 30_f64.sqrt();
// max_drawdown_limit comes from config; actual current drawdown requires
// P&L history from the trading repository which is not yet wired here.
let max_drawdown = self
.state
.config_repository
.get_config_f64("Risk", "max_drawdown")
.await
.map_err(|e| Status::internal(format!("Failed to get max drawdown: {}", e)))?
.map_err(|e| Status::internal(format!("Failed to get max drawdown config: {}", e)))?
.unwrap_or(0.10);
let metrics = RiskMetrics {
@@ -105,13 +151,17 @@ impl RiskService for RiskServiceImpl {
portfolio_var_5d,
portfolio_var_30d,
max_drawdown,
current_drawdown: 0.0, // Placeholder
sharpe_ratio: 1.5, // Placeholder
sortino_ratio: 2.0, // Placeholder
beta: 1.0, // Placeholder
alpha: 0.05, // Placeholder
volatility: 0.20, // Placeholder
position_risks: vec![], // TODO: Implement position risk calculation
// TODO: Compute current_drawdown from P&L history via trading_repository
current_drawdown: 0.0,
// TODO: Compute Sharpe, Sortino, beta, alpha from returns via trading_repository
sharpe_ratio: 1.5,
sortino_ratio: 2.0,
beta: 1.0,
alpha: 0.05,
// TODO: Derive volatility from recent return series via market_data_repository
volatility: 0.20,
// TODO: Populate position_risks from position_manager
position_risks: vec![],
};
Ok(Response::new(GetRiskMetricsResponse {
@@ -139,22 +189,60 @@ impl RiskService for RiskServiceImpl {
) -> Result<Response<ValidateOrderResponse>, Status> {
let req = request.into_inner();
// Basic validation logic
// Load configurable maximum order size from config repository.
// Falls back to 1,000,000 if not configured.
let max_order_quantity = self
.state
.config_repository
.get_config_f64("Risk", "max_order_quantity")
.await
.map_err(|e| {
warn!("Failed to load max_order_quantity from config: {}", e);
Status::internal(format!("Failed to load risk config: {}", e))
})?
.unwrap_or(1_000_000.0);
let mut violations = vec![];
let mut is_valid = true;
// Check maximum order size
if req.quantity > 1_000_000.0 {
// Check maximum order size against configurable limit
if req.quantity > max_order_quantity {
violations.push(RiskViolation {
violation_type: RiskViolationType::PositionLimit as i32,
description: "Order size exceeds maximum limit".to_string(),
description: format!(
"Order size {:.2} exceeds maximum limit {:.2}",
req.quantity, max_order_quantity
),
current_value: req.quantity,
limit_value: 1_000_000.0,
limit_value: max_order_quantity,
severity: RiskAlertSeverity::Critical as i32,
});
is_valid = false;
}
// Also check VaR limit via the real risk engine if a price is provided
if req.price > 0.0 && !req.symbol.is_empty() {
let risk_engine = self.state.risk_engine.read().await;
if let Err(var_err) = risk_engine
.check_var_limit(
&req.account_id,
&req.symbol,
req.quantity,
req.price,
)
.await
{
violations.push(RiskViolation {
violation_type: RiskViolationType::VarLimit as i32,
description: var_err.clone(),
current_value: req.quantity * req.price,
limit_value: risk_engine.max_var_limit(),
severity: RiskAlertSeverity::Critical as i32,
});
is_valid = false;
}
}
let risk_score = RiskScore {
overall_score: if is_valid { 3.0 } else { 8.0 },
concentration_score: 2.0,
@@ -186,12 +274,38 @@ impl RiskService for RiskServiceImpl {
) -> Result<Response<EmergencyStopResponse>, Status> {
let req = request.into_inner();
// Placeholder emergency stop implementation
// Delegate to the real TradingServiceKillSwitch when configured.
// If the kill switch system is not initialized, reject the call so callers
// know that the emergency stop is not operational rather than silently
// pretending success.
let kill_switch = self
.state
.kill_switch_system
.as_ref()
.ok_or_else(|| {
error!("Emergency stop called but kill_switch_system is not configured");
Status::unavailable(
"Kill switch system is not configured; emergency stop is unavailable",
)
})?;
error!("gRPC emergency_stop triggered: {}", req.reason);
kill_switch
.emergency_shutdown(req.reason.clone())
.await
.map_err(|e| {
error!("emergency_shutdown failed: {}", e);
Status::internal(format!("Emergency shutdown failed: {}", e))
})?;
info!("Emergency stop complete, global kill switch engaged");
Ok(Response::new(EmergencyStopResponse {
success: true,
message: format!("Emergency stop activated: {}", req.reason),
timestamp: chrono::Utc::now().timestamp(),
affected_orders: vec![], // TODO: Get actual affected orders
affected_orders: vec![], // TODO: Query order_manager for open orders at shutdown
}))
}