Candlesticks

AI Detection: 10 AI prompts for finance workflows

Use these AI Detection prompts to move from a rough finance task to a clearer, copy-ready AI workflow.

Edit highlighted fields Copy ready prompt

Copy-ready AI Detection finance prompts

Universal AI Candlestick Detection Pipeline Builder

Universal template Medium

Designs an end-to-end workflow to detect, interpret, and validate candlestick patterns using AI + rule-based checks.

ID 201

Act as a quant + AI engineer for trading. I want an AI-assisted pipeline to detect candlestick patterns for crypto market on 15-minute data using TradingView / Python / both. Design an end-to-end system: pattern taxonomy (which patterns, definitions, exclusions) data inputs (OHLCV, sessions, gaps) and labeling method detection approach (rule-based vs ML vs hybrid) outputs (signals, confidence, context notes) evaluation plan (precision/recall, false positives, stability across regimes) deployment plan (alerts, dashboards, monitoring, retraining triggers). Ask clarifying questions if needed.

Pine Script Pattern Detector Spec (AI-to-Pine Blueprint)

Pro

Turns your pattern definitions into a clean Pine Script design spec (before coding), including alerts and filters.

ID 202

Act as a TradingView Pine Script architect. I want to detect these candlestick patterns: list on 15-minute data. Write a detailed implementation spec (not code) that includes: exact candle rules (body/wick ratios, relative closes/opens, multi-candle relationships) strict edge cases to avoid false positives optional context filters (trend proxy, volatility threshold, session filter) alert logic (when to trigger, cooldown, de-duplication) on-chart visualization (labels, heatmap, table). End with a test checklist to validate the script visually on charts.

“Find Similar Candles in History” Pattern Matching Prompt

Beginner

Builds a similarity search workflow (what traders love: “show me past situations that looked like this”).

ID 203

Act as a pattern-matching research assistant. I will paste the last N candles as OHLC (or returns). Create a method to find the most similar historical sequences in ETF portfolio over years. Define: normalization method, similarity metric, how to avoid lookahead bias, how to rank matches, and what to report (next-move distribution, drawdown, best/worst cases). Output a simple step-by-step plan I can implement with Python/Sheets/TradingView.

Computer Vision Candlestick Detector Design (Chart Images)

Pro

Designs a CV approach (YOLO/vision model) to detect candlestick patterns on chart screenshots.

ID 204

Act as a computer vision engineer. I want to detect candlestick patterns from chart images (screenshots) for crypto market. Design a CV solution: dataset requirements, labeling format (bounding boxes vs sequence labels), model choice (detection vs classification), training split strategy, augmentation, and evaluation metrics. Also propose how to map detected patterns back to tradeable information (timestamp, price levels, confidence) and how to reduce overfitting to chart styles/themes.

Hybrid Detector: Rule-Based Patterns + AI Confidence Layer

Pro

Combines deterministic candlestick rules with an AI layer that learns when the rules are reliable.

ID 205

Act as a hybrid-systems designer. I want a two-layer detector: Layer 1 = strict rule-based candlestick pattern detection for patterns. Layer 2 = AI model that predicts whether the detected pattern is “high quality” given context (volatility, trend state, gap behavior, session). Define features, labeling method, target variable, and how to train/evaluate without leaking future information. Output a plan for how the two layers interact and when to override signals.

Auto-Labeling & Dataset Creation for Candlestick Patterns

Pro

Builds a practical labeling workflow (the hardest part) so you can train models without chaos.

ID 206

Act as a dataset engineer for trading ML. I want to build a labeled dataset for candlestick patterns on crypto market across symbols and 15-minute data. Design an auto-labeling workflow using rule definitions as the first pass, plus a human review loop. Specify: labeling schema, class balance plan, ambiguity rules (what to do when patterns overlap), quality checks, and how to version datasets and keep labels consistent over time.

Detection Quality Audit (False Positives, Ambiguity, Overlap)

Medium

Audits an existing pattern detector (Pine or ML) and explains what it’s getting wrong and why.

ID 207

Act as a signal-quality auditor. Here is how my detector defines patterns: paste rules or model behavior. Design an audit process to measure: false positive rate, ambiguous detections, duplicate detections, pattern overlap confusion, and sensitivity to parameter changes. Then propose 5 specific fixes (rule refinements, thresholds, cooldowns, context filters, class merges) prioritized by impact.

Explainability Prompt: “Why Did AI Flag This Pattern?”

Medium

Forces interpretability: converts a black-box “pattern detected” into human-readable reasons and checks.

ID 208

Act as an explainability layer for candlestick detection. Given a detected pattern pattern name on 15-minute data, produce a human-readable explanation: candle-by-candle measurements, which criteria were met, what was borderline, and what would invalidate it. Add a “confidence rationale” section and a “common failure modes” section for this exact pattern.

Robustness & Regime Shift Test for Candlestick AI

Medium

Tests whether your AI is stable across different volatility regimes and market conditions.

ID 209

Act as an ML robustness tester. I have a candlestick pattern model/detector for crypto market. Design a robustness test suite: split data by regimes (high vol/low vol, trending/ranging, crisis periods), test across symbols, test different timeframes, and measure performance drift. Output rules for when to retrain, when to disable signals, and how to prevent overfitting to one “era.”

Short & Sharp: “Build a Pattern Detector From This Definition”

Pro

Fast prompt to turn a written pattern definition into strict detection rules + edge cases.

ID 210

Convert this candlestick pattern definition into strict detection rules with edge cases and exclusions: Paste definition Output: exact numeric conditions, a minimal version (simple) and strict version (high precision), plus 3 examples of false positives to block.

Copy the full subcategory

Includes subcategory info, prompt IDs, descriptions, difficulty, and prompt text.