A comprehensive research infrastructure with: Backtesting Framework Event-driven architecture Transaction cost modeling Performance analytics Risk management integration Factor Research Platform Factor return calculation Statistical significance testing Cross-sectional analysis Factor decay modeling Signal Generation Technical indicators Factor-based signals Machine learning integration Signal combination methods Machine Learning Integration Time series cross-validation Feature engineering Model selection and validation … Continue reading Comprehensive Quant Workflow 6 Backtest and Research

Trading and Execution: Trading Cost Estimation Implementation shortfall calculation Market impact modeling using square root model Timing cost estimation Optimal Execution Strategy Almgren-Chriss optimal execution model Time-weighted average price (TWAP) implementation Volume-weighted average price (VWAP) considerations Dark Pool Integration Smart order routing across venues Historical fill rate analysis Price improvement optimization Position Unwinding Risk-aware liquidation … Continue reading Comprehensive Quant Workflow 5 Trading and Execution

Reporting and Analytics: Client reporting Risk reports Performance attribution reports Portfolio characteristics What-if analysis tools Real-time dashboards Hence, Main ReportingAnalytics Class, Acts as the main interface for all reporting and analytics functionality, Integrates all other components into a single, cohesive system Component Classes: ClientReporting: Handles generation and export of client reports RiskAnalytics: Implements risk metrics … Continue reading Comprehensive Quant Workflow 4 Reporting

This framework is to apply dynamic portfolio rebalancing can include: 1. Dynamic Rebalancing Triggers: Drift threshold monitoring; Scheduled rebalancing; Cash buffer breaches; Corporate action triggers; 2. Tax-Aware Rebalancing: Tax lot tracking; Tax loss harvestin opportunities; Holding period consideration; 3. Cash Management: Minimum cash buffer maintenance; Cash flow handling from corporate actions; Trade adjustment to maintain cash positions; 4. Corporate Action Driven Rebalancing: Dividend … Continue reading Comprehensive Quant Workflow 3 Portfolio Rebalancing

This comprehensive portfolio optimization framework integrates with the existing risk management and analytics system. Here's what's included: PortfolioOptimizer class with multiple optimization strategies: Maximum Sharpe Ratio Minimum Volatility Maximum Return Risk Parity Key Features: Flexible constraint handling through OptimizationConstraint dataclass Efficient frontier generation and visualization Integration with RiskAnalytics for risk metrics Integration with RiskManager for risk constraints Comprehensive … Continue reading Comprehensive Quant Workflow 2 Portfolio Optimization

Risk analytics and Decomposition import numpy as np import pandas as pd from typing import Dict, List, Tuple, Optional from dataclasses import dataclass import statsmodels.api as sm @dataclass class FactorExposures: market_beta: float size: float value: float momentum: float quality: float volatility: float class RiskAnalytics: def __init__(self, returns: pd.DataFrame, factor_returns: pd.DataFrame = None, benchmark_returns: pd.Series = … Continue reading Comprehensive Quant Workflow 1 Risk Management

comprehensive overview: Risk Analytics and Decomposition: Factor risk decomposition (market beta, size, value, momentum, etc.) Tracking error analysis vs benchmark Stress testing and scenario analysis Value at Risk (VaR) calculations Expected shortfall/Conditional VaR Risk attribution by factor/sector/asset Portfolio Optimization: Mean-variance optimization Black-Litterman model implementation Risk parity portfolio construction Transaction cost optimization Multi-period optimization Robust optimization … Continue reading Comprehensive Quant Workflow 0

The advent of artificial intelligence has rendered many once-unassailable businesses vulnerable. Rapid advances in technology have democratized access to information and tools, empowering individuals with the right mindset to challenge established norms. I am a relentless disrupter with a deep understanding of how the knowledge and strategies that once protected these businesses are now fully … Continue reading New Era of AI

There exists a collection of servers available in MCP GitHub: modelcontextprotocol/servers: Model Context Protocol Servers. However, this documentation primarily focuses on the code necessary to construct a weather service MCP server, which I intend to catalog here for future reference. As the number of contributors will increase, MCP has the potential to harness the capabilities … Continue reading Building a Weather Service Model Context Protocol (MCP) Server

An engineer at Tinybird already built a MCP server so it's good to follow along and grasp the nuances and intricacies in leveraging MCP. But more time-saving to go to MCP github. Before jumping to server.py, go over concepts again decorator, decorator such as @measure_time is classical, in class, note @property is a built-in decorator that transforms a … Continue reading Following a Real Case: Building a Model Context Protocol (MCP) Server