CRISP‑TIQ™ Methodology 

Phase 1: Trading Context & Objectives

Objective

Define the real-world constraints, goals, and profile of the trader or strategy sponsor.

Key Tasks

• Identify trader type (retail, prop, institutional)

• Define strategic goals (monthly return, drawdown tolerance, capital limits)

• Determine available timeframes and instruments

• Clarify risk tolerance, max drawdown, recovery tolerance

• Capture execution realities (manual vs. automated, broker constraints, latency)

Deliverables

• Trading Mission Brief

• Time Horizon + Capital Model

• Objective Definition Sheet (Win Rate, R-Multiple targets, frequency)

• Constraints Checklist (compliance, broker, slippage, tax, position sizing)

Iteration Triggers

• Change in account size or risk profile

• New market access (e.g., new broker or instrument)

• Pivot in trading philosophy (e.g., from mean-reversion to breakout)

Phase 2: Data Exploration & Feature Discovery

Objective

Understand the structure, availability, and quality of market data. Derive meaningful features and filters to feed into strategy logic.

Key Tasks

• Gather raw price data (OHLCV)

• Ingest alternative data (sentiment, options, macro indicators)

• Clean missing/duplicate/illiquid data

• Identify volatility regimes, structural shifts

• Visualize relationships between indicators and outcomes

• Construct candidate features (indicators, filters, custom ratios)

Deliverables

• Raw + Cleaned Data Files

• Exploratory Data Report

• Feature Library Draft

• Labeling Logic for Strategy Class (trend, mean-reversion, volatility breakout)

Iteration Triggers

• Poor signal performance in backtests

• Discovery of higher-quality data or new alternative source

• Changing market regime (requiring re-profiling)

Phase 3: Strategy Engineering

Objective

Translate the market hypothesis into a well-defined set of rules, signals, filters, and conditions.

Key Tasks

• Define entry and exit conditions (price behavior, indicator thresholds)

• Select filter logic (volatility bands, time-of-day, macro overlays)

• Design risk model (position sizing, stop loss logic, R-multiple targeting)

• Create pseudocode of strategy logic

• Map logic to quantifiable variables

Deliverables

• Strategy Design Document (TIQ template)

• Entry/Exit Logic Diagram

• Risk Management Model

• Complete Pseudocode

• Strategy Checklist (modularity, measurability, repeatability)

Iteration Triggers

• Failed logic tests in backtest or walkforward

• Discovery of contradictory filter logic

• Unclear edge or lack of signal frequency

Phase 4: Strategy Coding & Backtesting

Objective

Implement the strategy in code, backtest it on historical data, and validate key logic paths.

Key Tasks

• Select backtesting platform (QuantConnect, Backtrader, NinjaTrader, custom)

• Translate logic to code (modular functions)

• Test for logical bugs, overfitting flags, or data snooping

• Run backtests over multiple timeframes and instruments

• Store all test results with parameter/version tracking

Deliverables

• Strategy Codebase (Python or platform-native)

• Backtest Performance Summary

• Equity Curve(s)

• Error/Warning Log

• Parameter Tracker

Iteration Triggers

• Backtest metrics don’t meet objective targets

• Strategy performs too well (suspected overfitting)

• Slippage/execution unrealistic in backtest logic

Phase 5: Performance Evaluation

Objective

Objectively measure the strategy’s robustness, risk-adjusted returns, and deployment readiness.

Key Tasks

• Run walkforward validation

• Monte Carlo simulation of trade sequences

• Evaluate strategy robustness (parameter sensitivity, regime shifts)

• Compute advanced metrics (Sharpe, Sortino, CAGR, max DD, MAR ratio)

• Compare to benchmark (SPY, risk-free rate, buy-and-hold)

• Stress test for black swan, low-liquidity scenarios

Deliverables

• Walkforward Test Results

• Robustness Map

• Risk-to-Return Summary

• Strategy Readiness Scorecard

Iteration Triggers

• High drawdown volatility

• Inconsistent walkforward results

• Failure to generalize to out-of-sample data

Phase 6: Deploy & Monitor

Objective

Deploy the strategy in a live or simulated environment and establish a system for ongoing monitoring, performance capture, and refinement.

Key Tasks

• Choose deployment method (paper trade, live with small size, broker API)

• Automate execution logic (cron jobs, cloud deployment, signal streaming)

• Monitor live trades (logs, fills, slippage, latency)

• Implement error handling + alert systems

• Review strategy monthly/quarterly

• Re-train or re-optimize if necessary

Deliverables

• Live Execution Dashboard (via TIaaS or custom)

• Performance Tracker (real vs. backtest)

• Error & Alert System

• Post-Deployment Review Report

Iteration Triggers

• Significant deviation from expected metrics

• Infrastructure failure or broker API change

• Market condition shift invalidating strategy assumptions