import { NextRequest, NextResponse } from 'next/server';
import { exec } from 'child_process';
import { promisify } from 'util';
import path from 'path';
const execAsync = promisify(exec);
/**
* Black-Litterman Allocation API
* Phase 36: Combine market equilibrium with investor views for portfolio construction
*/
const MODULES_DIR = path.join(process.cwd(), 'modules');
const BL_MODULE = path.join(MODULES_DIR, 'black_litterman.py');
export async function GET(request: NextRequest) {
try {
const searchParams = request.nextUrl.searchParams;
const action = searchParams.get('action') || 'help';
let command: string;
switch (action) {
case 'black-litterman': {
const tickers = searchParams.get('tickers');
const views = searchParams.get('views');
const confidence = searchParams.get('confidence') || '0.25';
const period = searchParams.get('period') || '1y';
if (!tickers) {
return NextResponse.json({
error: 'Missing required parameter: tickers (comma-separated)'
}, { status: 400 });
}
command = `python3 ${BL_MODULE} black-litterman --tickers ${tickers} --confidence ${confidence} --period ${period}`;
if (views) {
command += ` --views ${views}`;
}
break;
}
case 'equilibrium-returns': {
const tickers = searchParams.get('tickers');
const period = searchParams.get('period') || '1y';
if (!tickers) {
return NextResponse.json({
error: 'Missing required parameter: tickers'
}, { status: 400 });
}
command = `python3 ${BL_MODULE} equilibrium-returns --tickers ${tickers} --period ${period}`;
break;
}
case 'portfolio-optimize': {
const tickers = searchParams.get('tickers');
const targetReturn = searchParams.get('target-return');
const allowShort = searchParams.get('allow-short') === 'true';
const period = searchParams.get('period') || '1y';
if (!tickers) {
return NextResponse.json({
error: 'Missing required parameter: tickers'
}, { status: 400 });
}
command = `python3 ${BL_MODULE} portfolio-optimize --tickers ${tickers} --period ${period}`;
if (targetReturn) {
command += ` --target-return ${targetReturn}`;
}
if (allowShort) {
command += ` --allow-short`;
}
break;
}
case 'help':
default: {
return NextResponse.json({
service: 'Black-Litterman Allocation',
phase: 36,
description: 'Combine market equilibrium with investor views for portfolio construction',
endpoints: {
'black-litterman': {
description: 'Full Black-Litterman model with investor views',
params: {
tickers: 'Comma-separated ticker symbols (required)',
views: 'Views as TICKER:RETURN,... (e.g., AAPL:0.15,MSFT:0.10)',
confidence: 'View confidence 0-1 (default: 0.25)',
period: 'Historical data period (default: 1y)'
},
example: '/api/v1/black-litterman?action=black-litterman&tickers=AAPL,MSFT,GOOGL&views=AAPL:0.20,GOOGL:0.12'
},
'equilibrium-returns': {
description: 'Derive implied equilibrium returns from market cap weights',
params: {
tickers: 'Comma-separated ticker symbols (required)',
period: 'Historical data period (default: 1y)'
},
example: '/api/v1/black-litterman?action=equilibrium-returns&tickers=SPY,QQQ,IWM'
},
'portfolio-optimize': {
description: 'Mean-variance optimization (max Sharpe ratio or target return)',
params: {
tickers: 'Comma-separated ticker symbols (required)',
'target-return': 'Target annual return (optional, default: max Sharpe)',
'allow-short': 'Allow short positions (default: false)',
period: 'Historical data period (default: 1y)'
},
example: '/api/v1/black-litterman?action=portfolio-optimize&tickers=AAPL,MSFT,GOOGL&target-return=0.15'
}
},
theory: {
'Reverse Optimization': 'Derive equilibrium returns π = δΣw from market cap weights',
'Black-Litterman Formula': 'E[R] = [(τΣ)⁻¹ + P\'Ω⁻¹P]⁻¹[(τΣ)⁻¹π + P\'Ω⁻¹Q]',
'Posterior Covariance': 'Σ_post = Σ + M where M is precision matrix',
'Mean-Variance Optimization': 'Maximize Sharpe ratio or achieve target return with minimum variance'
},
references: [
'Black, F. and Litterman, R. (1992) "Global Portfolio Optimization"',
'He, G. and Litterman, R. (1999) "The Intuition Behind Black-Litterman Model Portfolios"'
],
parameters: {
tau: 'Uncertainty scalar (default: 0.025)',
delta: 'Risk aversion coefficient (default: 2.5)',
omega: 'View uncertainty matrix (diagonal assumption)'
}
});
}
}
// Execute Python module
const { stdout, stderr } = await execAsync(command, {
cwd: process.cwd(),
timeout: 90000, // 90 second timeout for data fetching
});
if (stderr && !stderr.includes('FutureWarning')) {
console.error('Python stderr:', stderr);
}
try {
const result = JSON.parse(stdout);
return NextResponse.json(result);
} catch (parseError) {
return NextResponse.json({
error: 'Failed to parse Python output',
raw: stdout,
stderr: stderr
}, { status: 500 });
}
} catch (error: any) {
console.error('Black-Litterman API error:', error);
return NextResponse.json({
error: error.message || 'Internal server error',
details: error.toString()
}, { status: 500 });
}
}
