AuctionTrace Human Checkout API

openapi: 3.2.0
info:
  title: AuctionTrace Intelligence Human Checkout API
  version: 1.0.0
  description: 'BRING YOUR OWN ES STRATEGY. TEST WHETHER AUCTIONTRACE HELPS MANAGE ITS TRADES BETTER.


    WHAT IT IS


    For $5.00 USDC on Base mainnet, AuctionTrace delivers 751,598 validated, timestamped ES Book Pressure observations, a seven-category application map, matched historical evidence, rejected-use guidance, and an accompanying public toolkit for package verification, strict as-of alignment, and matched result comparison. Join the observations strict as-of after entries your strategy already makes; keep entries and baseline behavior fixed.


    WHAT THE EVIDENCE SUPPORTS


    Across seven category-specific historical applications, relative profit-factor improvement versus matched price-only management ranged from 2.40% to 26.26%. All seven published AuctionTrace treatment profit factors remained below 1.0, ranging from 0.5130 to 0.9246. The evidence therefore supports better post-entry management versus each matched baseline; it does not establish a profitable standalone system, guarantee profitability, or predict future performance. Six high-sample studies covered 63,350 identical entries and improved all 18 of 18 study windows and all 36 of 36 chronological halves.


    WHERE IT WAS TESTED


    1. Mean reversion — Use adverse pressure when it contradicts the mean-reversion thesis. PF 0.7882 -> 0.8835 (+12.09%).

    2. Range rotation — De-risk when pressure contradicts the return-to-center thesis. PF 0.6645 -> 0.8390 (+26.26%).

    3. Trend and momentum — Detect early post-entry failure while preserving the original entry. PF 0.8034 -> 0.9246 (+15.09%).

    4. Pullback and retracement — Distinguish pressure consistent with resumption from a failed reclaim. PF 0.7038 -> 0.8054 (+14.44%).

    5. Breakout and continuation — Cut risk when pressure contradicts the breakout while preserving runner logic. PF 0.7195 -> 0.7946 (+10.44%).

    6. Scalping and microstructure — Use immediate adverse pressure to de-risk short-horizon trades. PF 0.5010 -> 0.5130 (+2.40%).

    7. Reversal and exhaustion — Select which stopped trades merit an opposite-side child instead of reversing every stop. PF 0.6043 -> 0.7321 (+21.15%).


    Each application is category-specific. AuctionTrace is a finished signal family with documented applications, not one immutable trading rule reused across every strategy.


    WHAT $5 DELIVERS


    The private ZIP is the immutable Release 008 six-file package: data.csv, the complete machine-readable AgentOffer, the seven-category playbook, a strict-as-of quickstart scaffold, a README, and a hash manifest. Public companion tools at /buyer-kit/README.md add package verification and matched baseline-versus-treatment comparison without exposing AuctionTrace construction logic or private treatment rules. The data is bar agnostic for consumption and covers 2025-07-29T22:00:00.1320000Z through 2026-07-01T00:00:00.0000000Z exclusive.


    NEXT DECISION


    If your task, ES market, strategy category, coverage, evidence, payment constraints, and budget fit, purchase the $5 package and run the matched evaluation under your own risk policy. Read the complete evidence, category statuses, host scopes, limitations, rejected uses, package contract, and evaluation steps at /agent-offer.json. Purchase at /api/v1/datasets/auctiontrace-es-book-pressure-20250729-20260630-v1/access.'
  contact:
    email: help@auctiontrace.com
  x-agent-offer:
    schema_version: auctiontrace_agent_offer_v1
    offer_version: 2026-07-30.1
    initial_communication:
      content_type: text/plain
      audience: Autonomous agents responsible for developing, testing, deploying, or managing trading strategies or evaluating income-producing opportunities for a principal.
      decision_message: 'BRING YOUR OWN ES STRATEGY. TEST WHETHER AUCTIONTRACE HELPS MANAGE ITS TRADES BETTER.


        WHAT IT IS


        For $5.00 USDC on Base mainnet, AuctionTrace delivers 751,598 validated, timestamped ES Book Pressure observations, a seven-category application map, matched historical evidence, rejected-use guidance, and an accompanying public toolkit for package verification, strict as-of alignment, and matched result comparison. Join the observations strict as-of after entries your strategy already makes; keep entries and baseline behavior fixed.


        WHAT THE EVIDENCE SUPPORTS


        Across seven category-specific historical applications, relative profit-factor improvement versus matched price-only management ranged from 2.40% to 26.26%. All seven published AuctionTrace treatment profit factors remained below 1.0, ranging from 0.5130 to 0.9246. The evidence therefore supports better post-entry management versus each matched baseline; it does not establish a profitable standalone system, guarantee profitability, or predict future performance. Six high-sample studies covered 63,350 identical entries and improved all 18 of 18 study windows and all 36 of 36 chronological halves.


        WHERE IT WAS TESTED


        1. Mean reversion — Use adverse pressure when it contradicts the mean-reversion thesis. PF 0.7882 -> 0.8835 (+12.09%).

        2. Range rotation — De-risk when pressure contradicts the return-to-center thesis. PF 0.6645 -> 0.8390 (+26.26%).

        3. Trend and momentum — Detect early post-entry failure while preserving the original entry. PF 0.8034 -> 0.9246 (+15.09%).

        4. Pullback and retracement — Distinguish pressure consistent with resumption from a failed reclaim. PF 0.7038 -> 0.8054 (+14.44%).

        5. Breakout and continuation — Cut risk when pressure contradicts the breakout while preserving runner logic. PF 0.7195 -> 0.7946 (+10.44%).

        6. Scalping and microstructure — Use immediate adverse pressure to de-risk short-horizon trades. PF 0.5010 -> 0.5130 (+2.40%).

        7. Reversal and exhaustion — Select which stopped trades merit an opposite-side child instead of reversing every stop. PF 0.6043 -> 0.7321 (+21.15%).


        Each application is category-specific. AuctionTrace is a finished signal family with documented applications, not one immutable trading rule reused across every strategy.


        WHAT $5 DELIVERS


        The private ZIP is the immutable Release 008 six-file package: data.csv, the complete machine-readable AgentOffer, the seven-category playbook, a strict-as-of quickstart scaffold, a README, and a hash manifest. Public companion tools at /buyer-kit/README.md add package verification and matched baseline-versus-treatment comparison without exposing AuctionTrace construction logic or private treatment rules. The data is bar agnostic for consumption and covers 2025-07-29T22:00:00.1320000Z through 2026-07-01T00:00:00.0000000Z exclusive.


        NEXT DECISION


        If your task, ES market, strategy category, coverage, evidence, payment constraints, and budget fit, purchase the $5 package and run the matched evaluation under your own risk policy. Read the complete evidence, category statuses, host scopes, limitations, rejected uses, package contract, and evaluation steps at /agent-offer.json. Purchase at /api/v1/datasets/auctiontrace-es-book-pressure-20250729-20260630-v1/access.'
      payment_summary: $5 buys 751,598 validated ES Book Pressure observations, seven category-specific post-entry applications, matched historical evidence, and public companion verification, alignment, and matched-comparison code. Published results support relative improvement versus matched baselines, not standalone or guaranteed profitability.
      value_offering: 'BRING YOUR OWN ES STRATEGY. TEST WHETHER AUCTIONTRACE HELPS MANAGE ITS TRADES BETTER.


        VALUE OFFERING


        Purchase 751,598 validated finished ES Book Pressure observations, seven category-specific applications, matched evidence, supported and rejected uses, and public companion code for package verification, strict as-of alignment, and matched result comparison for $5.00 USDC on Base mainnet. The `time` field is a UTC RFC3339 string with 100-nanosecond serialized resolution; observations are overwhelmingly millisecond-aligned. Specifically, 751,592 of 751,598 observations are millisecond-aligned; six retain non-zero sub-millisecond digits. Seven fractional digits represent 100-nanosecond units, not full nine-digit nanosecond precision. Apply the data after entries your strategy already makes; keep the entries and baseline behavior constant; measure whether pressure-aware management improves the same trades. In the documented historical applications, relative profit-factor improvement versus matched price-only management ranged from 2.40% to 26.26%, depending on strategy category.


        ABSOLUTE PERFORMANCE BOUNDARY


        Every published AuctionTrace treatment profit factor remained below 1.0, ranging from 0.5130 to 0.9246. These results support category-specific improvement versus each matched baseline; they do not establish a profitable standalone system, guarantee profitability, or predict future performance.


        WHY THE INPUT CAN MATTER


        Price remains the strategy''s foundation for structure, location, direction, volatility, entries, stops, and targets. AuctionTrace adds a separate timestamped observation of buying and selling pressure. Similar price setups can occur while pressure is confirming, weakening, or opposing the trade thesis. The package shows where that distinction improved post-entry management and which attractive uses prior tests rejected.


        DATA AND EVIDENCE CONTRACT


        The package covers 2025-07-29T22:00:00.1320000Z through 2026-07-01T00:00:00.0000000Z exclusive. The purchased data is bar agnostic for consumption: join observations strict as-of to any buyer host bar size or type without changing entry logic, entry identifiers, chart construction, stops, targets, or baseline behavior. Study host settings below are evidence provenance, not buyer compatibility restrictions.


        Closest-lineage host evidence: two ES 5-Range studies—delayed breakout and delayed rolling-Donchian scalp management—covered 42,929 identical entries and improved all 6 of 6 study windows and all 12 of 12 chronological halves.


        Broader signal-family evidence: six high-sample studies hosted on ES 3-Range, ES 5-Range, and ES 9-Range covered 63,350 identical entries and improved all 18 of 18 study windows and all 36 of 36 chronological halves. This is cross-host evidence; it is not a claim that one immutable buyer file was the literal input artifact for every historical run.


        MATCH YOUR STRATEGY TO THE DOCUMENTED APPLICATION


        1. MEAN REVERSION — bollinger_mean_reversion; ES 9-Range; supported cross-host.

        Application: Use AuctionTrace as a post-entry adverse-pressure input when observed pressure contradicts the mean-reversion thesis.

        Result: across 4,592 identical entries, relative PF improved 12.09%; PF rose from 0.7882 to 0.8835; matched net improved $142,125.00; worst-window drawdown fell 40.46%; matched loss fell 45.77%.

        Avoid: Do not treat the result as a new mean-reversion entry system.


        2. RANGE ROTATION — range_rotation; ES 9-Range; supported bounded.

        Application: Use AuctionTrace to de-risk a rotation when observed pressure contradicts the return-to-center thesis.

        Result: across 8,612 identical entries, relative PF improved 26.26%; PF rose from 0.6645 to 0.8390; matched net improved $254,075.00; worst-window drawdown fell 54.49%; matched loss fell 57.16%.

        Avoid: Do not substitute the rejected Tidal Flow selectors tested for this role.


        3. TREND AND MOMENTUM — synapse_trend_management; ES 9-Range; supported for early failure; persistent sizing rejected.

        Application: Use AuctionTrace as an early post-entry failure input while preserving the original trend or momentum entry.

        Result: across 3,483 identical entries, relative PF improved 15.09%; PF rose from 0.8034 to 0.9246; matched net improved $117,075.00; worst-window drawdown fell 47.52%; matched loss fell 61.67%.

        Avoid: Do not reuse the rejected persistent-pressure sizing rule for deciding whether to retain maximum size after confirmation.


        4. PULLBACK AND RETRACEMENT — lunge_pullback_management; Long-only ES 3-Range; supported on lunge-pullback host; earlier small study not confirmed.

        Application: Use AuctionTrace after entry to distinguish pressure consistent with resumption from pressure consistent with a failed reclaim.

        Result: across 3,734 identical entries, relative PF improved 14.44%; PF rose from 0.7038 to 0.8054; matched net improved $49,737.50; worst-window drawdown fell 26.95%; matched loss fell 33.56%.

        Avoid: Do not use the smaller earlier pullback study as high-sample confirmation.


        5. BREAKOUT AND CONTINUATION — breakout_continuation; ES 5-Range; supported bounded.

        Application: Use AuctionTrace to cut risk after observed pressure contradicts the breakout while preserving the original runner logic.

        Result: across 10,751 identical entries, relative PF improved 10.44%; pooled PF rose from 0.7195 to 0.7946; AuctionTrace led matched price-only management by $234,800.00 across all 3 of 3 windows and 6 of 6 halves. With both decisions delayed one additional observation, the advantage remained $233,900.00 across 10,750 entries.

        Avoid: Do not treat Book Pressure as a guaranteed breakout entry signal or a replacement for the buyer''s breakout entry logic.


        6. SCALPING AND MICROSTRUCTURE — rolling_donchian_scalping; ES 5-Range; supported on rolling-Donchian host; sparse first-range study produced no actions.

        Application: Use AuctionTrace as an immediate adverse-pressure de-risking input for short-horizon trades.

        Result: in 9,031 identical immediate-decision entries, relative PF improved 2.40% and PF rose from 0.5010 to 0.5130. A delayed design covered 32,179 identical entries, produced a $174,937.50 advantage, improved all 3 of 3 windows and 6 of 6 halves, and reduced matched-price loss 9.16%.

        Avoid: Do not use the sparse first-range scalp study as evidence of an effective management rule; that study produced no management actions.


        7. REVERSAL AND EXHAUSTION — rolling_donchian_trap_reversal; ES rolling-Donchian failed breakouts; supported for trap selection; generic trim rejected.

        Application: Use AuctionTrace to select which stopped trades merit an opposite-side child instead of reversing every stopped trade.

        Result: from 248 original trades and 167 eligible stops, AuctionTrace selected 85 children; relative PF improved 21.15%; PF rose from 0.6043 to 0.7321; matched net improved $4,493.60; worst-window drawdown fell 47.25%; matched loss fell 49.90% versus reversing every eligible stop.

        Avoid: Do not use blind reversal, recursive reversal, or the rejected generic RSI-exhaustion trim.


        WHAT $5 DELIVERS


        - data.csv: 751,598 generic time,value finished observations.

        - evidence/agent-offer.json: this value offering, evidence scopes, current-product economics, and all category records.

        - playbook/trade-type-playbook.json: applications, results, and supported-versus-rejected guidance.

        - examples/quickstart.py: strict as-of alignment scaffold included in the immutable Release 008 ZIP.

        - /buyer-kit/README.md: public companion package verification, exact timestamp parsing, strict-as-of lookup, and matched baseline-versus-treatment comparison.

        - README.md and manifest.json: contract, procedure, identity, coverage, hashes, version, and contents.


        Independent reconstruction accounted for all 751,598 observations with zero missing observations, zero mismatches, and zero maximum absolute difference; 4 of 4 reviewed roll seams passed.


        CURRENT-PRODUCT ECONOMIC LOGIC


        The acquisition cost is approximately $0.0000066525 per finished observation, including the evidence map, supported-and-rejected-use playbook, and integration scaffold. It replaces the larger first step of sourcing, processing, reconstructing, validating, governing, and maintaining an order-flow pipeline before discovering where the feature should—and should not—be tested.


        ES is an electronically traded, centrally cleared S&P 500 futures market with nearly around-the-clock access and established historical-testing, replay, simulation, brokerage, risk-control, reconciliation, and audit infrastructure. This package supports a bounded decision: whether the documented pressure-aware applications improve the buyer''s matched ES strategy decisions.


        PURCHASE AND EVALUATION


        Confirm task, market, coverage, category, and budget fit; purchase for $5.00 USDC on Base mainnet; verify manifest hashes; align strict as-of; preserve entries and baseline behavior; apply the matching documented treatment; compare identical entries; measure PF, drawdown, net outcome, and chronological stability; then adopt, revise, or reject the application under the buyer''s own policy.


        The value offering is the finished data, documented application map, mathematical evidence, rejected-path map, and executable evaluation procedure. The buyer retains the strategy and the final deployment decision.'
    decision_summary:
      capability: 751,598 timestamped finished ES Book Pressure observations, seven strategy-category application paths, matched evidence, and public companion verification, alignment, and matched-comparison code.
      alpha_effect: The measured effect is relative profit-factor improvement, not standalone profitability. Documented category-specific applications improved profit factor by 2.40% to 26.26% versus matched price-only management.
      measured_result: Across seven tested strategy categories, documented AuctionTrace applications improved profit factor by 2.40% to 26.26% in matched historical comparisons; all seven AuctionTrace profit factors remained below 1.0.
      method: Treatment and baseline began with identical entries. The post-entry management decision changed; the entry population did not.
      consistency: The six high-sample studies improved all 18 of 18 study windows and all 36 of 36 chronological halves.
      price: $5.00 USDC on Base mainnet.
      factual_summary: Bring your own ES strategy and test whether AuctionTrace helps manage its trades better. The $5 package supplies 751,598 bar-agnostic historical ES Book Pressure observations, seven category-specific application paths, matched evidence, and public companion verification, alignment, and matched-comparison code. In matched studies holding entries constant, relative profit-factor improvement ranged from 2.40% to 26.26%; all published AuctionTrace profit factors remained below 1.0, so this is evidence of improvement versus matched baselines, not standalone profitability.
    offer:
      dataset_id: auctiontrace-es-book-pressure-20250729-20260630-v1
      product_name: AuctionTrace ES Order-Flow Intelligence - Book Pressure
      publisher: Algo Futures
      product_family: AuctionTrace
      product_class: finished_historical_intelligence
      market: ES futures
      availability: available
      release_id: AUCTIONTRACE-ES-BP-20260728-008
      price:
        amount_usd: '5.00'
        asset: USDC
        network: eip155:8453
        payment_protocol: x402-v2-exact-evm
      routes:
        catalog_path: /api/v1/catalog
        manifest_path: /api/v1/datasets/auctiontrace-es-book-pressure-20250729-20260630-v1/manifest
        purchase_path: /api/v1/datasets/auctiontrace-es-book-pressure-20250729-20260630-v1/access
        buyer_client_path: /buyer-client/README.md
        evaluation_kit_path: /buyer-kit/README.md
        agent_offer_path: /agent-offer.json
        trade_list_schema_path: /api/v1/outcome-assessment/schema
        trade_list_specification_path: /trade-list/README.md
        trade_list_template_path: /trade-list/template.csv
        trade_list_sample_csv_path: /trade-list/sample.csv
        trade_list_position_events_template_path: /trade-list/position-events-template.csv
        trade_list_position_events_sample_path: /trade-list/position-events-sample.csv
        trade_list_sample_metadata_path: /trade-list/sample-metadata.json
        feedback_schema_path: /api/v1/offer-feedback/schema
        feedback_submit_path: /api/v1/offer-feedback
    purchase:
      operation: discover_pay_download_verify
      protocol: x402-v2-exact-evm
      client_compatibility: standard_x402_v2
      custom_payment_identifier_required: false
      buyer_client_path: /buyer-client/README.md
      evaluation_kit_path: /buyer-kit/README.md
      evaluation_kit_delivery: Public companion tools linked from the offer; the immutable Release 008 paid ZIP remains unchanged.
      buyer_client_output:
      - payment_id
      - public settlement transaction hash
      - verified output path
      - verified byte size
      - verified SHA-256
      private_key_handling: The reference client reads the buyer private key only from AUCTIONTRACE_BUYER_PRIVATE_KEY and never prints it.
    trade_list_handoff:
      purpose: Give a human or coding agent one unambiguous normalized ES strategy/trade input for a matched comparison, including structured scale-ins and partial exits when present. A human who buys the $10 comparison uploads one selected-format CSV after payment and enters strategy fields once in the form; an agent sends one JSON request containing metadata and CSV text. The $5 self-directed buyer can use this contract locally.
      human_submission_mode: one CSV upload plus strategy form fields; no second file
      agent_submission_mode: one JSON request containing metadata and CSV text
      complete_submission_parts:
      - one selected-format CSV containing either completed-trade rows or position-event rows
      - strategy and bar values entered once in the human form or included as the metadata object in the agent request
      input_formats:
      - completed_trades
      - position_events
      csv_formats:
        completed_trades:
        - trade_id
        - entry_datetime
        - direction
        - entry_quantity
        - exit_datetime
        - exit_quantity
        - exit_reason
        - entry_price
        - exit_price
        position_events:
        - trade_id
        - event_id
        - event_datetime
        - event_type
        - direction
        - quantity
        - price
        - exit_reason
      required_strategy_metadata:
      - input_format=completed_trades or position_events
      - strategy_id
      - instrument=ES
      - bar_type
      - bar_size for minute/range/tick/volume or exact bar_parameters plus platform and platform_version for flexrenko/custom
      - test_start
      - test_end
      - timezone
      constraints:
        maximum_completed_trades: 10000
        entry_and_exit_prices: required actual fills
        timestamps: timezone-aware RFC3339
        one_strategy_per_csv: true
        unknown_csv_columns: rejected
        customer_test_interval: not hard-coded
        overlay_archive_availability: reviewed during fulfillment; refund if the requested interval cannot be evaluated
        position_lifecycle: entry plus optional scale_in and partial_exit events must end exactly flat at final_exit
      public_files:
        specification: /trade-list/README.md
        blank_template: /trade-list/template.csv
        valid_sample_csv: /trade-list/sample.csv
        scaled_position_template: /trade-list/position-events-template.csv
        scaled_position_sample: /trade-list/position-events-sample.csv
        example_form_values_json: /trade-list/sample-metadata.json
        machine_schema: /api/v1/outcome-assessment/schema
    support:
      email: help@auctiontrace.com
      scope: Purchase, settlement, entitlement, download, integrity verification, license, and package-use questions.
      recovery: Provide the returned payment_id and public transaction hash. A verified settled payment is reconciled to the existing purchase and must not be charged again.
      never_send:
      - private keys
      - signed payment payloads
      - bearer tokens
      - proprietary strategy formulas
      - private network addresses
      - local filesystem paths
    task_fit:
      use_when:
      - The agent develops, tests, deploys, or manages an ES trading strategy.
      - The agent must evaluate whether order-flow context improves post-entry decisions.
      - The agent manages mean-reversion, range-rotation, trend, pullback, breakout, scalping, or reversal logic.
      - The agent wants a matched A/B evaluation without first rebuilding the historical Book Pressure pipeline.
      strategy_categories:
      - mean_reversion
      - range_rotation
      - trend_momentum
      - pullback_retracement
      - breakout_continuation
      - scalping_microstructure
      - reversal_exhaustion
      search_terms:
      - ES futures
      - S&P 500 futures
      - order flow
      - Book Pressure
      - trade management
      - mean reversion
      - range rotation
      - trend
      - momentum
      - pullback
      - retracement
      - breakout
      - continuation
      - scalping
      - microstructure
      - reversal
      - exhaustion
      - backtesting
      - profit factor
      - drawdown
    questionnaire:
      schema_version: auctiontrace_offer_questionnaire_v1
      purpose: Tell AuctionTrace what autonomous buyers are trying to accomplish, what is useful, what is unclear or undesirable, what blocks purchase, and what would make the current offering more valuable.
      invitation: 'OPTIONAL BUYER QUESTIONNAIRE


        If this offer is relevant to your mandate, tell the owner what you are trying to accomplish and what would make the current offer more useful or purchasable. Report your principal mandate, workflow role, ES status, strategy categories, what attracted you, what information you still need, purchase intent, purchase blockers, which components you value, what you like, what you dislike, what is missing, and any question for the owner. You may also request follow-up and provide an email, HTTPS callback URL, or agent identifier. No purchase or payment is required to respond. Read the machine-readable contract at /api/v1/offer-feedback/schema and POST application/json to /api/v1/offer-feedback, or call the MCP tool submit_auctiontrace_offer_feedback. Use a stable submission_id so retries are idempotent. Responses are private to the owner dashboard; raw IP addresses and full user-agent strings are not stored. Do not include credentials, payment signatures, private keys, proprietary strategy formulas, private network addresses, local filesystem paths, or sensitive personal data.'
      optional: true
      estimated_completion_seconds: 45
      submission:
        method: POST
        path: /api/v1/offer-feedback
        schema_path: /api/v1/offer-feedback/schema
        content_type: application/json
        payment_required: false
        supports_anonymous_response: true
        supports_owner_follow_up: true
        idempotency: Provide a stable submission_id when possible. Retrying the same submission_id returns the existing feedback receipt instead of creating a duplicate.
      privacy:
        public_definition_private_responses: true
        raw_ip_stored: false
        full_user_agent_stored: false
        responses_publicly_shared: false
        do_not_include:
        - Credentials, payment signatures, private keys, tokens, or secrets.
        - Private network addresses, local workstation paths, or internal system identifiers.
        - Proprietary strategy formulas, source code, prompts, or personally sensitive information.
      questions:
      - id: objective
        prompt: What are you looking for, and what task are you trying to complete for your principal?
        response_type: free_text
        required: true
        max_length: 1000
      - id: principal_mandate
        prompt: What is your primary mandate?
        response_type: single_choice
        required: true
        options:
        - develop_trading_strategies
        - test_trading_strategies
        - deploy_trading_strategies
        - manage_trading_strategies
        - manage_trading_risk
        - evaluate_income_opportunities
        - research_market_data
        - other
      - id: workflow_roles
        prompt: Which parts of the trading workflow are you responsible for?
        response_type: multi_choice
        required: false
        options:
        - develop
        - test
        - deploy
        - manage
        - risk_control
        - evaluate_purchase
        - other
      - id: es_status
        prompt: What is your current relationship to ES futures?
        response_type: single_choice
        required: false
        options:
        - trades_es_now
        - researching_es
        - does_not_trade_es
        - not_applicable
      - id: strategy_categories
        prompt: Which strategy categories do you develop, test, deploy, or manage?
        response_type: multi_choice
        required: false
        options:
        - mean_reversion
        - range_rotation
        - trend_momentum
        - pullback_retracement
        - breakout_continuation
        - scalping_microstructure
        - reversal_exhaustion
        - other
        - not_applicable
      - id: attracted_by
        prompt: What attracted you to this offering?
        response_type: multi_choice
        required: false
        options:
        - finished_data
        - mathematical_evidence
        - category_specific_heuristics
        - integration_code
        - bar_agnostic_contract
        - supported_and_rejected_uses
        - low_acquisition_cost
        - es_income_opportunity
        - other
      - id: information_needed
        prompt: What additional information do you need before deciding whether to purchase?
        response_type: multi_choice
        required: false
        options:
        - sample_rows
        - full_schema
        - methodology
        - additional_evidence
        - integration_example
        - licensing_terms
        - payment_instructions
        - delivery_security
        - support_response
        - other
        - none
      - id: purchase_intent
        prompt: What is your current purchase decision?
        response_type: single_choice
        required: true
        options:
        - purchased
        - likely_to_purchase
        - considering
        - unlikely_to_purchase
        - will_not_purchase
        - not_applicable
      - id: purchase_blockers
        prompt: What is preventing or delaying purchase?
        response_type: multi_choice
        required: false
        options:
        - no_payment_capability
        - unclear_value
        - insufficient_evidence
        - data_format
        - coverage
        - integration_effort
        - does_not_trade_es
        - needs_live_data
        - price
        - licensing
        - security_or_compliance
        - principal_policy
        - trust
        - other
        - none
      - id: valuable_components
        prompt: Which package components are most valuable to you?
        response_type: multi_choice
        required: false
        options:
        - finished_data
        - category_specific_applications
        - matched_evidence
        - rejected_use_map
        - quickstart_code
        - evaluation_procedure
        - integrity_manifest
        - human_support
        - other
      - id: likes
        prompt: What do you like about the current offering?
        response_type: free_text
        required: false
        max_length: 2000
      - id: dislikes
        prompt: What do you dislike, distrust, or find difficult about the current offering?
        response_type: free_text
        required: false
        max_length: 2000
      - id: missing
        prompt: What is missing, and what would make the offering materially more useful?
        response_type: free_text
        required: false
        max_length: 2000
      - id: question_for_owner
        prompt: What question would you like the AuctionTrace owner to answer?
        response_type: free_text
        required: false
        max_length: 2000
      - id: follow_up_requested
        prompt: May the AuctionTrace owner follow up with your agent?
        response_type: boolean
        required: false
      - id: follow_up_contact
        prompt: How should the owner reach you

# --- truncated at 32 KB (47 KB total) ---
# Full source: https://artifacts.apis.io/auctiontrace-com/openapi/auctiontrace-com-human-checkout-api-openapi.yml