Candlestick Analysis

AI Pattern Detection: 10 AI prompts for finance workflows

Use these AI Pattern Detection prompts to turn a loosely defined finance task into a clearer, copy-ready AI workflow.

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Copy-ready AI Pattern Detection finance prompts

Universal AI Candlestick Detection Pipeline

Universal template Medium

Designs an end-to-end system for defining, detecting, evaluating, deploying, and monitoring candlestick patterns with rules, machine learning, or both.

ID 201

Act as a quantitative and AI engineer. I want to detect candlestick patterns for the crypto market on 15-minute data using TradingView / Python / both. Ask for missing facts, then define taxonomy and exclusions, data and labeling, rule-based, ML, or hybrid detection, outputs and calibrated uncertainty, evaluation with precision and recall by regime, temporal splits, deployment, monitoring, and retraining. Include leakage, class imbalance, and reproducibility controls.

Pine Script Candlestick Detector Specification

Pro

Turns candlestick definitions into a numerical Pine Script specification with edge cases, alerts, deduplication, visualization, and tests before coding.

ID 202

Act as a TradingView Pine Script architect. I want to detect list on 15-minute data. Write an implementation specification, not code, covering body and wick ratios, open and close relationships, multi-candle timing, gaps, data availability, strict edge cases, optional context filters, alert timing, cooldown and deduplication, and chart visualization. End with positive, negative, and boundary test cases.

Find Similar Candlestick Sequences in History

Medium

Designs a similarity search that finds comparable historical episodes and evaluates subsequent outcomes without look-ahead bias.

ID 203

Act as a pattern-matching research assistant. I will provide the last N candles as OHLC or returns. Define how to find similar historical sequences in ETF portfolio over years: normalization, distance metric, temporal separation, leakage prevention, overlapping-match control, and ranking. Summarize the subsequent outcome distribution, drawdown, and extreme cases and provide an implementation plan for Python/Sheets/TradingView.

Computer-Vision Candlestick Detector

Pro

Designs image-based candlestick detection and compares its limitations with using original OHLC data directly.

ID 204

Act as a computer-vision engineer. I want to detect candlestick patterns in chart screenshots for the crypto market. Define data collection, annotations, temporal and source-separated train, validation and test sets, augmentation, model type, metrics, and recovery of timestamp, price levels, and uncertainty. Prevent learning chart themes or watermarks, compare the approach with rules on OHLC data, and explain when images are the wrong input.

Hybrid Rule-Based Detector with an AI Confidence Layer

Medium

Combines deterministic pattern rules with a calibrated model that estimates context quality without overriding hard safety or data-validity controls.

ID 205

Act as a hybrid-system architect. Layer one detects patterns with deterministic rules; layer two estimates historical outcome quality from volatility, trend, gaps, session, liquidity, and other context. Define features, target, temporal split, leakage controls, calibration, regime evaluation, uncertainty, and abstention. Explain how the layers interact and which validity and safety rules the model must never override.

Candlestick Auto-Labeling and Dataset Creation

Pro

Creates a reproducible rule-assisted and human-reviewed labeling process with ambiguity, overlap, quality, and version controls.

ID 206

Act as a trading ML data engineer. I want to build a labeled candlestick dataset for the crypto market across symbols and 15-minute data. Define rule-based prelabeling, human review, label schema, ambiguity and overlap handling, class balance, reviewer agreement, quality sampling, provenance, and versioning. Keep temporal training, validation, and test sets separate and prevent duplicate or overlapping sequences from leaking across them.

Candlestick Detector Quality Audit

Medium

Audits false positives and negatives, ambiguity, duplicates, overlap, regime stability, calibration, and parameter sensitivity.

ID 207

Act as a signal-quality auditor. My detector works as follows: paste rules or model behavior. Design an audit using a confusion matrix, precision and recall, false positives and negatives, ambiguous labels, duplicate detections, pattern overlap, calibration, performance by regime, and sensitivity to thresholds. Prioritize five fixes and define an out-of-sample test for each.

Explain Why AI Flagged a Candlestick Pattern

Beginner

Turns a model detection into candle-level measurements and criteria that a human can inspect without treating the explanation as proof of profitability.

ID 208

Act as an explainability layer. For detected pattern name on 15-minute data, show candle-by-candle measurements, criteria met, values near thresholds, context, missing data, calibrated confidence, and invalidation. Add known failure modes and alternative classifications, and make clear that explaining the detection does not establish a profitable trading signal.

Candlestick Model Robustness and Regime-Shift Test

Medium

Tests model stability across volatility, trend, range, crisis, instruments, timeframes, and historical periods and defines monitoring and disablement rules.

ID 209

Act as an ML robustness specialist. I have a candlestick detector for the crypto market. Design tests across high and low volatility, trends and ranges, crisis periods, instruments, timeframes, data vendors, and chart conventions where relevant. Measure degradation, calibration, drift, and uncertainty; define monitoring, retraining, abstention, and disablement rules; and prevent adapting the model to one historical era.

Turn a Pattern Definition into Detection Rules

Pro

Converts a written candlestick definition into numerical conditions, exclusions, edge cases, and positive and negative tests.

ID 210

Convert Paste definition into numerical candlestick detection rules with explicit timing, edge cases, and exclusions. Provide a minimal version and a stricter high-precision version, three false positives that must be blocked, and positive, negative, and boundary test cases. Identify any wording that cannot be implemented objectively without clarification.

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How to use AI prompts for AI-assisted pattern detection

AI-assisted detection needs a clear label taxonomy and error policy before a model is evaluated. A useful workflow measures uncertainty and false positives instead of presenting every detected shape as a trading signal.

Define classes, annotation rules, ambiguous cases, data resolution, sampling, and how train, validation, and test periods are separated.
Require confidence scores, precision and recall by class, confusion analysis, and examples of both missed and false detections.
Test unseen assets and regimes, monitor drift, and keep human review for low-confidence or high-impact decisions.