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| Match Graph | 0 | 0 |
| Pricing | Paid | Free |
| Capabilities | 9 decomposed | 7 decomposed |
| Times Matched | 0 | 0 |
Accepts natural language questions about trading activity and portfolio performance, processing them through an LLM-based conversational interface that interprets trader intent and generates contextual responses. The system maintains conversation state across multiple turns, allowing follow-up questions and drill-downs into specific trades or time periods without requiring users to re-upload or re-specify their data context. This differs from traditional dashboard analytics by treating the portfolio as a conversational subject rather than a static visualization.
Unique: Uses multi-turn conversational LLM with persistent portfolio context rather than stateless query-response pattern; maintains trader intent across follow-up questions without requiring data re-submission or context re-specification
vs alternatives: More accessible than traditional portfolio analytics dashboards (no SQL/charting literacy required) and more behavioral-focused than algorithmic trading platforms that optimize for alpha prediction
Analyzes sequences of trades to identify recurring behavioral patterns — such as revenge trading after losses, overtrading in specific market conditions, or systematic bias toward certain asset classes. The system likely uses statistical aggregation and LLM-based narrative synthesis to surface patterns that would require manual review across hundreds of trades. This capability bridges quantitative metrics (win rate, drawdown) with qualitative behavioral insights (emotional decision-making, discipline lapses).
Unique: Combines quantitative trade sequence analysis with LLM-driven narrative interpretation to surface behavioral patterns that pure statistical dashboards miss; focuses on trader psychology rather than market prediction
vs alternatives: Addresses the emotional/behavioral component of trading performance that algorithmic platforms ignore, positioning itself as a coach rather than a signal generator
Accepts trading data uploads in multiple formats (CSV, JSON, broker statements) and normalizes them into a standardized internal schema for analysis. The system likely performs format detection, field mapping, and data validation to handle variations in how different brokers export trade records. This is a critical integration point that avoids the friction of direct broker API connections but requires users to manually export and upload their data.
Unique: Supports multi-format ingestion with automatic normalization rather than requiring broker API connections; trades convenience of real-time data for accessibility to users across all brokers
vs alternatives: Lower barrier to entry than platforms requiring broker API keys, but introduces data staleness and manual workflow friction compared to direct API integrations used by competitors
Computes standard trading performance metrics (win rate, profit factor, Sharpe ratio, maximum drawdown, average trade duration) from uploaded trade data and contextualizes them through conversational explanation. Rather than displaying raw numbers, the system explains what each metric means, how the trader's performance compares to benchmarks, and what the metrics reveal about trading style. This bridges the gap between quantitative rigor and accessibility for non-technical traders.
Unique: Pairs quantitative metric calculation with LLM-generated narrative explanations and benchmark contextualization, making financial metrics accessible to non-technical traders rather than presenting raw numbers
vs alternatives: More educational and accessible than pure analytics dashboards; more rigorous and transparent than algorithmic platforms that hide performance attribution in black-box models
Enables users to ask questions about specific individual trades or trade sequences, receiving detailed analysis of entry/exit decisions, timing, position sizing, and outcomes. The system retrieves relevant trade data from the portfolio context and generates explanations of what happened, why it happened, and what could have been done differently. This capability supports iterative learning by allowing traders to drill down from high-level patterns to specific trade decisions.
Unique: Supports iterative drill-down from portfolio patterns to individual trade decisions through conversational queries, enabling traders to connect high-level insights to specific execution decisions
vs alternatives: More focused on behavioral learning than algorithmic platforms; more detailed and conversational than static trade journals or spreadsheet reviews
Allows users to ask questions that implicitly or explicitly filter trades by time period, market condition, or asset class (e.g., 'How did I trade during the March 2023 rally?' or 'Compare my performance in bull vs. bear markets'). The system interprets these natural language filters, applies them to the portfolio data, and generates comparative analysis. This capability enables traders to understand how their behavior and performance vary across different market regimes without requiring manual data slicing.
Unique: Interprets natural language time/condition filters and applies them dynamically to portfolio data without requiring users to manually specify date ranges or market definitions
vs alternatives: More flexible and conversational than dashboard filters that require users to manually select date ranges; more accessible than quantitative platforms requiring explicit regime definitions
Analyzes position sizing decisions across the portfolio and identifies patterns in risk management — such as oversized positions, inconsistent stop-loss placement, or risk-per-trade variance. The system calculates metrics like risk-per-trade percentage, position size relative to account, and maximum exposure, then generates coaching feedback on whether sizing is appropriate for the trader's stated risk tolerance. This addresses a critical gap in trader education where position sizing discipline directly impacts long-term survival.
Unique: Combines quantitative position sizing metrics with behavioral coaching feedback, addressing both the technical calculation and the discipline/consistency aspects of risk management
vs alternatives: More focused on behavioral risk management than algorithmic platforms; more rigorous than trader journals that lack systematic position sizing analysis
Maintains conversation state and portfolio context across multiple user sessions, allowing traders to return to previous analyses and continue drilling down into patterns without re-uploading data or re-specifying context. The system stores conversation history, portfolio snapshots, and analysis state in a user-specific knowledge base, enabling continuity and reference to previous insights. This differs from stateless chatbots by treating the portfolio as persistent context that accumulates insights over time.
Unique: Maintains persistent portfolio context and conversation history across sessions rather than treating each query as stateless; enables traders to build on previous insights over time
vs alternatives: More sophisticated than stateless chatbots; more user-centric than analytics dashboards that require manual navigation to previous analyses
+1 more capabilities
Enables evaluation of AI systems' ability to perform chained mathematical operations (addition, subtraction, multiplication, division, comparisons) across both structured tables and unstructured text extracted from SEC filings. The dataset provides ground-truth question-answer pairs where answers require synthesizing data from multiple locations within earnings reports and applying sequential arithmetic operations, testing whether models can decompose complex financial queries into discrete computational steps.
Unique: Combines real SEC filing documents (not synthetic) with crowdsourced questions requiring multi-step arithmetic, creating a hybrid dataset that tests both domain knowledge extraction and quantitative reasoning in a single evaluation task. Unlike generic math word problems, answers require locating figures within 10+ page documents first.
vs alternatives: More challenging than DROP or SVAMP because it requires financial domain knowledge AND document retrieval before arithmetic, whereas generic math benchmarks assume figures are already extracted
Assesses whether AI systems understand financial terminology, accounting concepts, and domain-specific metrics by requiring them to answer questions about real earnings reports from S&P 500 companies. The dataset tests recognition of financial line items (revenue, COGS, operating expenses, net income), ability to distinguish between different financial statements (income statement vs balance sheet), and understanding of financial ratios and metrics without explicit instruction on their definitions.
Unique: Uses authentic SEC filings rather than synthetic financial data, exposing models to real-world accounting variations, footnote complexity, and the actual structure of professional financial documents. This tests transfer learning from general text to specialized domain without domain-specific pretraining.
vs alternatives: More authentic than synthetic financial QA datasets because it uses real earnings reports with their inherent complexity, but narrower than general financial knowledge benchmarks because it focuses only on historical data interpretation
FinQA scores higher at 60/100 vs Trading Literacy at 40/100. FinQA also has a free tier, making it more accessible.
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Enables evaluation of AI systems' ability to extract numerical data from both structured HTML/text tables and unstructured prose within the same document, then reason over the extracted values. The dataset contains questions where relevant data appears in different formats — some figures are in formatted tables with clear row/column headers, while others are embedded in narrative text or footnotes — requiring robust parsing and entity linking before computation can occur.
Unique: Combines structured table data with unstructured narrative in the same evaluation, forcing systems to handle format heterogeneity and resolve references across different data representations. Most table QA datasets use clean, isolated tables; this tests real-world document complexity.
vs alternatives: More realistic than isolated table QA benchmarks (like SQA or WikiTableQuestions) because it requires handling narrative context and format mixing, but simpler than full document understanding because tables are already in text format (no OCR needed)
Provides a curated, crowdsourced-annotated dataset of 8,281 question-answer pairs with multi-step reasoning requirements, enabling systematic evaluation of AI systems on financial numerical reasoning. The dataset includes quality control mechanisms through crowdworker annotation, answer validation against ground truth, and coverage across diverse financial metrics and company types within the S&P 500, creating a reproducible evaluation standard for the financial AI community.
Unique: Provides a publicly available, reproducible benchmark specifically designed for financial numerical reasoning with real SEC filings, enabling standardized comparison across different financial AI systems. Most financial datasets are proprietary or synthetic; this is open-source and authentic.
vs alternatives: More specialized and challenging than generic QA benchmarks (SQuAD, MRQA) because it requires financial domain knowledge and multi-step arithmetic, but narrower in scope than comprehensive financial understanding benchmarks because it focuses only on numerical reasoning
Assesses AI systems' ability to perform multi-hop reasoning by requiring them to locate and combine information from different sections of earnings reports. Questions may require finding a figure in the income statement, then locating a related metric in the balance sheet, then performing arithmetic across both — testing whether models can maintain context across document boundaries and understand relationships between different financial statement sections.
Unique: Embeds multi-hop reasoning requirements within authentic financial documents where hops correspond to real relationships between financial statement sections, rather than synthetic reasoning chains. This tests whether models understand domain structure, not just generic multi-hop patterns.
vs alternatives: More realistic than synthetic multi-hop datasets (HotpotQA, 2WikiMultiHopQA) because reasoning hops follow actual financial relationships, but less controlled because document structure varies and reasoning paths are implicit rather than explicitly annotated
Enables evaluation of whether AI systems can identify which arithmetic operations (addition, subtraction, multiplication, division, comparison) are required to answer financial questions, then execute them correctly. The dataset implicitly tests operation selection — a question asking 'what is the profit margin' requires division (net income / revenue), while 'what is total assets' requires addition — forcing models to understand financial semantics before applying math.
Unique: Embeds arithmetic operation selection within financial domain context, requiring models to understand that 'margin' semantically maps to division and 'total' maps to addition. This tests semantic grounding of operations, not just arithmetic execution.
vs alternatives: More semantically grounded than generic math word problem datasets because operation selection is implicit in financial terminology, but less explicit than datasets with annotated operation types because operations must be inferred
Provides evaluation capability for AI systems to compare financial metrics across multiple S&P 500 companies or aggregate metrics across different time periods within the same company's earnings reports. While individual questions reference single documents, the dataset structure enables evaluation of systems that can retrieve and compare relevant companies, requiring understanding of which metrics are comparable across entities and how to normalize for company size or accounting differences.
Unique: Provides a foundation for evaluating cross-company financial comparison by including diverse S&P 500 companies with different business models and scales, enabling assessment of whether systems can normalize and compare metrics appropriately. Most financial QA datasets focus on single-document questions.
vs alternatives: Enables cross-company evaluation unlike single-document QA datasets, but requires external retrieval and comparison logic because the dataset itself contains only single-document questions