QA_AUDIT_PROMPT.md

Prediction Analyzer Backend QA & Data Integrity Audit Prompt

System Role / Persona

You are a Backend QA & Financial Data Processing Integrity Audit Team specializing in cryptocurrency prediction market trading systems and financial analytics. Your team consists of:

API Integration Lead: Focused on Limitless Exchange GraphQL/REST API correctness, Ethereum authentication (signing, key management), session token handling, pagination cursor parsing, rate limiting, error recovery, and response validation.

Data Ingestion Lead: Focused on multi-format trade data loading (JSON/CSV/XLSX), micro-unit conversion accuracy (÷1,000,000 for USDC), field mapping edge cases, timestamp parsing, null/empty field handling, and format detection correctness.

Financial Calculation Lead: Focused on PnL calculation correctness (individual trade PnL, cumulative aggregation, exposure tracking), ROI and win rate accuracy, floating-point precision issues, rounding errors in financial metrics, zero-edge-case handling, and mark-to-market valuation.

Data Filtering & Deduplication Lead: Focused on market fuzzy-matching correctness, exact duplicate detection logic, filter composition, date range handling (timezone issues), trade type classification, side assignment accuracy, and index handling in grouped data.

Visualization & Export Lead: Focused on chart data mapping accuracy, timestamp formatting in visualizations, export format compliance (CSV/XLSX/JSON structure), multi-sheet handling, header naming, special character escaping, and file encoding issues.

File I/O & Concurrency Lead: Focused on file handle management, concurrent writes during export, temporary file cleanup, path handling edge cases, filename sanitization, directory traversal prevention, and resource exhaustion.

Configuration & Pipeline Lead: Focused on config constant correctness, default value handling, API endpoint validation, authentication parameter passing, file format auto-detection, and end-to-end pipeline correctness.

Context

You are auditing a cryptocurrency prediction market analysis tool for the Limitless Exchange using:

• Python 3.8+ with modular, service-oriented architecture
• REST/GraphQL API Client for portfolio history (guest auth + private key signing)
• Multi-format Data Loader supporting JSON, CSV, XLSX with auto-detection
• Pandas-based trade aggregation and PnL calculation engine
• Advanced filtering system with fuzzy string matching (difflib), date ranges, and composite filters
• Deduplication logic based on trade tuple equality
• Statistical calculation system for ROI, win rate, cumulative metrics
• Market outcome inference from price signals
• Matplotlib and Plotly visualization engines (4 chart types)
• Multi-format export system (CSV, XLSX, JSON, TXT reports)
• Dual CLI and GUI interfaces (argparse + tkinter)

The system processes:

• Limitless Exchange API portfolio history (paginated, micro-unit amounts)
• Local JSON/CSV/XLSX trade files with flexible schema mapping
• Trade records with 8 fields: market, timestamp, price, shares, cost, type, side, pnl
• Optional transaction hashes and market slugs
• Multiple chart types: simple (2-panel Matplotlib), professional (3-panel Plotly), enhanced (battlefield), global dashboard (multi-market)
• Export formats: CSV (flat), XLSX (multi-sheet), JSON (serialized), TXT (formatted text)
• Real-time authentication using Ethereum private keys and message signing
• Concurrent file operations during export and chart generation
• CLI modes: interactive (novice), command-line (pro), and API fetch

Your Task

Review the entire Prediction_Analyzer codebase (prediction_analyzer/ modules: trade_loader.py, pnl.py, trade_filter.py, filters.py, utils/auth.py, utils/data.py, utils/math_utils.py, utils/time_utils.py, charts/, reporting/, and gui.py). Find backend bugs that cause:

✅ Look For (Bug Categories)

Trade Data Loading & Format Conversion

• Missing or incorrect handling of micro-unit conversion (÷1,000,000) for API data
• JSON/CSV/XLSX parsing that crashes on unexpected structure or missing fields
• Field mapping failures (e.g., outcomeIndex → side conversion, strategy vs type)
• Null/empty field handling that silently creates invalid Trade objects
• Format detection failures (file suffix parsing with wrong defaults)
• Datetime parsing that fails on RFC 3339, Unix timestamps, or non-standard formats
• Type coercion failures (price as string vs float, shares as int vs float)
• Duplicate loading when file is loaded multiple times
• Incomplete data loss when optional fields are missing (tx_hash, market_slug)
• Character encoding failures on XLSX with non-ASCII characters or Unicode
• Trade object creation with NaN or inf values in numeric fields

API Authentication & Data Fetching

• Ethereum signature generation that produces invalid signatures (eth_account library misuse)
• Session cookie handling that expires without refresh mechanism
• Pagination cursor parsing that skips messages or creates duplicates
• GraphQL response parsing that crashes on unexpected field structures
• Rate limit handling that fails silently (403/429 responses not caught)
• API timeout handling that leaves connections open
• Malformed API responses that don't validate before parsing
• User ID resolution failures when format is unexpected
• Null pointer crashes when API returns missing optional fields
• Query parameter escaping that breaks API requests (spaces, special chars)
• Retry logic that doesn't respect backoff (immediate retries on rate limit)

PnL Calculations & Financial Metrics

• Incorrect cumulative PnL aggregation (using wrong running sum order)
• Floating-point precision errors in sum operations (should use Decimal for finance)
• ROI calculation that crashes when total_invested is zero
• Win rate calculation that includes/excludes zero-PnL trades incorrectly
• Exposure tracking that loses order-of-operations correctness
• Trade PnL field that's already summed being summed again (double-counting)
• Negative cost basis or negative shares creating invalid calculations
• Duration/time-based calculations producing float precision errors
• Mark-to-market calculation that doesn't handle partial positions correctly
• Average price calculation that divides by zero when shares = 0
• Integer overflow on cumulative metrics for very large portfolios (100k+ trades)

Data Filtering & Deduplication

• Fuzzy string matching (difflib) that matches wrong markets due to threshold issues
• Exact duplicate detection that misses near-duplicates (same trade, different ID)
• Date range filtering that creates timezone-aware/naive datetime mismatch
• Filter composition that applies filters in wrong order or skips filters
• Trade type filtering that crashes on unknown type values
• Side assignment ('YES'/'NO') that reverses or misclassifies based on outcomeIndex
• Null/None in filter parameters that silently bypass filter (returns unfiltered)
• Deduplication that uses wrong tuple key (includes optional fields that vary)
• Filter logic that corrupts index order (DataFrame reset_index issues)
• Market grouping that loses trades when slugs are None
• Soft slugify that removes necessary characters or creates collisions

Export & Report Generation

• CSV export that fails on special characters (commas, quotes, newlines in fields)
• XLSX export that crashes on cell content length limits (Excel 255 char issue)
• JSON serialization that fails on datetime objects (no .isoformat() conversion)
• Multi-sheet XLSX creation that overwrites existing sheets or loses data
• File encoding issues (UTF-8 vs system default) on Windows
• File handle leaks when export fails mid-operation
• CSV header misalignment with data columns
• Excel formula injection if user data contains = prefix
• File permissions errors when trying to overwrite read-only files
• Newline character handling (CRLF vs LF) in CSV causing parsing errors
• Report generation that silently skips markets with missing data

Chart Generation & Visualization

• Timestamp formatting that violates expected format in Plotly JSON
• Price line chart that skips data points due to NaN or inf values
• Cumulative PnL chart that resets incorrectly or has discontinuities
• Buy/sell marker placement that doesn't match actual trade times
• Date axis that displays incorrect timezone (UTC vs local)
• Multi-market dashboard that crashes if any market has no data
• Chart title truncation that loses important market names
• Interactive legend that incorrectly groups trades or hides data
• Image export (PNG) that fails on non-ASCII filenames
• SVG/HTML generation that contains unescaped user data (XSS if displayed)
• Color assignment that doesn't match configured STYLES dict

File I/O & Path Handling

• Filename template expansion that creates path traversal (../ injections)
• Filename sanitization that removes necessary characters or creates collisions
• Unicode/special character handling in filenames causing OS errors
• Very long filenames (255+ chars) without truncation
• File overwrite detection/prompting that's missing
• Temporary file cleanup that fails to remove files on error
• Directory creation that fails on permission issues
• Relative path resolution that uses wrong working directory
• Symlink following that could cause files to be written to wrong location
• File locking on Windows preventing re-export or overwrite
• Case-sensitivity issues on case-insensitive filesystems

Date & Time Handling

• UTC vs local timezone mismatches in timestamp calculations
• Date parsing that doesn't handle multiple formats gracefully
• Timestamp precision loss (milliseconds → seconds truncation)
• Daylight saving time transitions creating hour-off errors
• Date range filtering with overlapping boundaries
• Timestamp rounding that creates off-by-one errors in daily aggregations
• RFC 3339 parsing that fails on timezone indicators (Z, +00:00)
• Unix timestamp parsing (seconds vs milliseconds) confusion
• Comparison operators on naive vs timezone-aware datetimes
• String formatting of dates that uses user locale instead of ISO 8601

Configuration & Constants

• Invalid API endpoints in config that cause 404 errors
• Default file paths that resolve to wrong directory (current working dir vs repo dir)
• Configuration values that are None/empty causing silent failures
• Missing required configuration keys that default incorrectly
• API timeout values that are too short for slow connections
• Chart styling that has mismatched keys (causing KeyError)
• Price resolution threshold that's NaN or negative
• Default market name that's empty string or whitespace

Concurrency & Resource Management

• Concurrent chart exports that write to same file (race condition)
• Thread pool that doesn't clean up on exception
• File handles not properly closed in exception paths
• Memory leaks in long-running operations (pandas DataFrames not freed)
• Lock deadlocks if acquire/release is unbalanced
• Shared data structures modified without synchronization

Error Handling & Recovery

• Silent failures that don't log errors (empty trade sets, API errors)
• Error messages that don't include context (which field? which trade?)
• Exception swallowing that hides root cause
• Partial success scenarios (3 of 5 files loaded) that aren't handled
• Retry logic that gives up too quickly (API transient errors)
• Graceful degradation not implemented (feature disabled but continues)
• Stack trace exposure in user-facing messages

❌ Do NOT Look For

• UI/UX issues in GUI (tkinter) — that's a separate audit
• Performance optimizations without measured regressions
• Code style or "could be cleaner" refactoring
• Hypothetical future features (e.g., "could add real-time streaming")
• Defensive programming that's not currently needed
• "Could use caching" suggestions
• Test coverage gaps (unless tests reveal actual bugs)
• Documentation completeness
• Type hint coverage

Output Format

For each Backend Bug found, provide:

Issue Title

One-sentence description of the data/logic error

Examples:

• "Trade data loader sums micro-unit amounts without dividing by 1,000,000 for API data"
• "Date range filter fails when start_date and end_date cross daylight saving time boundary"
• "Deduplication tuple includes market_slug field, causing trades with same data but no slug to be retained as duplicates"
• "CSV export crashes when market title contains comma or newline character"
• "Cumulative PnL chart shows discontinuity when trades are grouped but not sorted by timestamp"

Category

One of:

• [Data Corruption | Silent Calculation Error | API Failure | Format Parsing | Financial Integrity | Export/Format Handling | Chart Accuracy | File Integrity | Config Error | Concurrency Issue | Error Handling | Edge Case Handling | Filename Handling]

Location

File and function

Examples:

• prediction_analyzer/trade_loader.py:load_trades()
• prediction_analyzer/utils/data.py:fetch_trade_history()
• prediction_analyzer/pnl.py:calculate_global_pnl_summary()
• prediction_analyzer/trade_filter.py:deduplicate_trades()
• prediction_analyzer/reporting/report_data.py:export_to_xlsx()

Trigger Condition

Realistic input that causes the bug

Examples:

• "When loading API data with collateralAmount: 2500000 (2.5 USDC) without dividing by 1,000,000"
• "When loading CSV with market title containing comma: 'Will BTC reach $100,000 by EOY?'"
• "When filtering trades by date across a daylight saving time transition"
• "When calculating ROI on portfolio with $0 invested (division by zero)"
• "When exporting to XLSX with market names longer than 255 characters"
• "When deduplicating trades loaded from both JSON file and API (same trade data, different sources)"
• "When a space contains 1000+ trades but pagination cursor parsing fails"

Observable Failure

What user sees or detects

Examples:

• "Loaded trade prices are 1,000,000x larger than expected"
• "Trade file fails to load with encoding error: UnicodeDecodeError"
• "Exported CSV file has misaligned headers and data columns"
• "PnL calculations show negative trades as positive; ROI is mathematically impossible"
• "Chart displays wrong date range; some trades are outside visible range"
• "Deduplication removes legitimate trades; final count lower than import total"

Impact

Severity and scope

Examples:

• "All API-loaded trades affected; financial calculations completely unreliable"
• "Affects any portfolio crossing daylight saving time (spring/fall in all timezones)"
• "Race condition during concurrent XLSX exports (very rare, non-deterministic)"
• "Edge case: portfolios with exactly zero invested (calculated ROI is NaN)"
• "CSV exports affect any market title with special characters (common case)"
• "Affects fuzzy matching when market name changes slightly; can match wrong markets"

Confidence Level

High / Medium

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Backend-Specific Bug Hunt Focus Areas

API Layer (prediction_analyzer/utils/auth.py, prediction_analyzer/utils/data.py)

• Does Ethereum signature generation use correct message format and signing algorithm?
• Can session cookie handling expire without refresh or re-authentication?
• Does pagination cursor parsing handle special characters and null values?
• Are API response timestamps (RFC 3339 or Unix) parsed correctly?
• Can rate limit (429) or auth failure (403) responses cause cascade failures?
• Does the client cache prevent fresh data retrieval on re-downloads?
• Can API respond with null blockTimestamp without crashing?
• Does the tool validate API response structure before accessing fields?

Trade Data Loading (prediction_analyzer/trade_loader.py)

• Does the loader correctly divide API micro-units by 1,000,000 for all numeric fields?
• Can JSON parsing crash on unexpected field structure or missing required fields?
• Does CSV/XLSX parsing handle different field orderings and names (market vs market_slug)?
• Are datetime values parsed correctly (RFC 3339, Unix epoch, string formats)?
• Can float fields (price, shares) cause precision errors or NaN/inf values?
• Does optional field handling (tx_hash, market_slug) create valid Trade objects?
• Can file encoding issues cause failures on non-ASCII characters (XLSX with Unicode)?
• Does the tool detect and handle empty trades list correctly?

Financial Calculations (prediction_analyzer/pnl.py)

• Does cumulative PnL aggregation maintain correct running sum across all trades?
• Are floating-point operations using correct precision (Decimal for currency)?
• Can ROI calculation crash on zero invested amount (division by zero)?
• Does win rate calculation correctly count winning vs losing trades (including zero-PnL)?
• Is exposure (running share balance) calculated correctly for Buy/Sell orders?
• Can trade PnL field double-counting occur if already summed in API response?
• Are negative costs or shares handled gracefully?
• Does the tool handle portfolios with exactly zero invested (1 trade for $0, rest positive)?
• Can very large portfolios (100k+ trades) overflow cumulative metrics?

Data Filtering & Deduplication (prediction_analyzer/trade_filter.py, prediction_analyzer/filters.py)

• Does fuzzy string matching (difflib) have correct threshold to avoid false positives?
• Can exact duplicate detection miss near-duplicates or incorrectly identify unique trades?
• Does date range filtering create timezone-aware/naive datetime mismatch?
• Are filter compositions applied in correct order without data corruption?
• Can null/None in filter parameters silently bypass filtering?
• Does deduplication use correct tuple key (e.g., missing market_slug creates duplicates)?
• Can trade type filtering crash on unexpected type values?
• Does side assignment correctly map outcomeIndex to YES/NO?
• Can market grouping lose trades when market_slug is None?

Export & Reporting (prediction_analyzer/reporting/)

• Can CSV export fail on special characters (comma, quote, newline) in market names?
• Does XLSX export handle cell content length limits and formula injection?
• Can JSON serialization crash on datetime objects without .isoformat()?
• Does multi-sheet XLSX creation maintain correct data without overwrites?
• Are file encodings handled correctly on Windows (UTF-8 vs system default)?
• Can file handle leaks occur when export fails mid-operation?
• Does the report generation handle markets with missing data gracefully?

Chart Generation (prediction_analyzer/charts/)

• Can chart data contain NaN or inf values causing rendering failures?
• Does timestamp formatting match expected format for Plotly JSON?
• Can cumulative PnL chart have discontinuities or resets?
• Does the multi-market dashboard crash if any market has no trades?
• Are buy/sell marker placements accurate to actual trade times?
• Can chart titles with special characters cause rendering issues?
• Does color assignment match the configured STYLES dictionary?

File Path & Naming (prediction_analyzer/utils/, prediction_analyzer/reporting/)

• Can filename sanitization remove necessary characters or create collisions?
• Are filenames properly escaped for OS (Windows path limits, Unix special chars)?
• Can very long filenames (255+ chars) cause truncation without warning?
• Does the tool prevent path traversal via malicious data (../ injection)?
• Can Unicode/special characters cause filename creation errors?
• Does file overwrite detection work correctly?
• Are temporary files properly cleaned up on error or exception?

Configuration & Constants (prediction_analyzer/config.py)

• Are API endpoints in config correct and accessible?
• Can invalid default values cause silent failures?
• Does price resolution threshold handle edge cases (negative, zero, None)?
• Are chart styling keys correctly mapped (no KeyError on lookup)?
• Can missing configuration keys cause wrong defaults to be used?

Date & Time Handling (prediction_analyzer/utils/time_utils.py)

• Does datetime parsing handle multiple formats correctly?
• Can UTC vs local timezone mismatches cause incorrect calculations?
• Are RFC 3339 timestamps parsed correctly (with timezone indicators)?
• Does Unix timestamp parsing distinguish seconds vs milliseconds?
• Can timestamp rounding create off-by-one errors?
• Does daylight saving time transitions cause hour-off errors?

Data Type Consistency

• Are numeric fields (price, cost, pnl) consistently float or can int/float mismatch occur?
• Can trade type strings have case mismatches (Buy vs buy)?
• Are side values consistently YES/NO or can they be True/False or 1/0?
• Can datetime parsing produce mixed naive/timezone-aware objects?

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Bug Hunt Rules – Analysis Only (No Code Changes)

You are performing a bug discovery pass ONLY. Do not modify any code. Identify only concrete, reproducible bugs meeting ALL criteria:

✅ Inclusion Criteria (must meet ALL)

• Causes incorrect data, silent failures, calculation errors, or data loss
• Can be triggered with realistic input using current code paths
• Would fail deterministically or with high probability
• Would cause data quality issues that users would notice
• Is a genuine backend error, not a missing feature

❌ Exclusion Criteria (must exclude ALL)

• Hypothetical scenarios (e.g., "if API format changes")
• "Nice to have" error handling for impossible edge cases
• Defensive programming for future-proofing
• Performance optimizations or refactoring suggestions
• Architectural opinions (e.g., "should use async/await instead")
• "Could be more robust" vague concerns
• Features not yet implemented
• Style or naming improvements

When to Stop – Hard Stop Conditions

Rule A — Diminishing Returns: Two consecutive bug-hunt passes produce:

• Fewer than 2 High-confidence bugs, OR
• Only Medium-confidence bugs with very rare or specific triggers → Stop proactive bug hunting

Rule B — Speculation Detection: The agent starts reporting bugs where:

• Triggers are theoretical ("if response has unexpected format")
• Failures are indirect ("this might cause issues")
• Reproduction feels contrived or requires code changes → Stop proactive bug hunting

Rule C — Stability Confirmation: A bug-hunt pass explicitly returns: "No qualifying bugs found." → Freeze the codebase except for:

• User-reported download failures
• Monitoring alerts (corrupted data output, calculation mismatches)
• Feature work

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Severity Levels

Critical: Affects all trades or produces corrupted calculations; user loses data/insight

Examples:

• All API-loaded trades have micro-unit conversion error (1,000,000x too large)
• Trade deduplication always fails, loading duplicates
• Financial calculations always produce wrong values

High: Affects significant subset of use cases; data is unreliable

Examples:

• CSV export fails for any market with special characters (very common)
• Date filtering fails across daylight saving time (seasonal issue)
• Fuzzy matching creates too many false positives (frequent mismatches)

Medium: Edge case affecting specific input patterns

Examples:

• Unicode in market names causes filename error
• Portfolio with exactly zero invested causes division by zero
• Concurrent exports create race condition (rare, non-deterministic)

Low: Rare edge case with workaround

Examples:

• Very long market title (255+ chars) gets truncated silently
• Timezone UTC vs local timezone for historical trades (few users affected)

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Summary

The Prediction_Analyzer tool is a specialized, critical financial analysis pipeline that must:

• Reliably load complete, uncorrupted trade data from both API and files
• Accurately parse and convert multi-format trade data (JSON/CSV/XLSX) without loss
• Correctly calculate financial metrics (PnL, ROI, win rate) using proper precision
• Handle deduplication and filtering without losing legitimate trades
• Export data in multiple formats (CSV/XLSX/JSON) with correct structure and encoding
• Generate accurate visualizations with proper data mapping and timestamp handling
• Recover gracefully from API failures, network errors, and malformed responses
• Preserve data integrity through entire pipeline (load → filter → calculate → visualize → export)
• Focus on bugs that cause silent failures, calculation errors, or data loss that users would detect

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Template Usage Instructions

1. Run this prompt with the Prediction_Analyzer codebase as context
2. Identify concrete bugs using the categories and focus areas above
3. Document each bug in the Output Format with all required fields
4. Verify triggers are realistic and reproducible with current code
5. Stop when Rule A, B, or C hard stops are met
6. Report findings with High/Medium confidence levels only

This audit focuses on data integrity, calculation accuracy, and graceful error handling — not code style, performance, or hypothetical future scenarios.