| --- |
| license: apache-2.0 |
| language: |
| - en |
| pretty_name: Qandy 1.0 27B Training and Evaluation Data |
| size_categories: |
| - 10K<n<100K |
| task_categories: |
| - text-generation |
| tags: |
| - finance |
| - crypto |
| - trading |
| - perpetual-futures |
| - reasoning |
| - kto |
| - preference |
| - starburst |
| configs: |
| - config_name: sft |
| data_files: |
| - split: train |
| path: sft/train.jsonl |
| - config_name: kto |
| data_files: |
| - split: train |
| path: kto/train.jsonl |
| - config_name: holdout_real |
| data_files: |
| - split: test |
| path: eval/holdout_real.jsonl |
| - config_name: backfill_10k |
| data_files: |
| - split: test |
| path: eval/backfill_10k.jsonl |
| --- |
| |
| # Qandy 1.0 27B: Training and Evaluation Data |
|
|
| The data used to train and evaluate [Qandy 1.0 27B](https://huggingface.co/QandyAI/qandy-1.0-27B), from Starburst, QANDY's trading research line. Each example pairs a compact market snapshot for one crypto perpetual-futures symbol with a decision: **LONG**, **SHORT** or **NO TRADE**. Trades come with leverage, a stop, a target and key levels, plus a step-by-step reasoning trace that cites only the signals in the snapshot. |
|
|
| The examples come from a live, high-leverage scalping desk. Losing trades and costly skips are kept and labelled rather than filtered out, so the data can teach a model what *not* to do as well as what to do. |
|
|
| | Config | Split | Rows | Used for | |
| |---|---|---|---| |
| | `sft` | train | 896 | Stage 1 supervised fine-tuning | |
| | `kto` | train | 1,384 | Stage 2 KTO preference tuning | |
| | `holdout_real` | test | 182 | Main evaluation on live desk outcomes | |
| | `backfill_10k` | test | 10,000 | Larger replay evaluation | |
|
|
| ```python |
| from datasets import load_dataset |
| |
| sft = load_dataset("QandyAI/qandy-1.0-27B-data", "sft", split="train") |
| kto = load_dataset("QandyAI/qandy-1.0-27B-data", "kto", split="train") |
| holdout = load_dataset("QandyAI/qandy-1.0-27B-data", "holdout_real", split="test") |
| ``` |
|
|
| ## Snapshot format |
|
|
| Every prompt is a two-message chat: a fixed system prompt and a user snapshot. |
|
|
| ```text |
| You are qwopus-trades, the entry-reasoning model for a crypto perp desk. From the snapshot, decide LONG, SHORT, or NO TRADE — reason through the signals, then give one decision with leverage, a risk plan (stop, target), and key levels (support below price, resistance above price). Cite only signals in the snapshot. |
| ``` |
|
|
| The snapshot carries the symbol, a reference price, and whichever of these signals were available at decision time: RSI (5m/15m/1h/4h), MACD 4h, Ichimoku, VWAP, funding versus its 70th percentile, open interest, spot and perp CVD, taker flow, whale flow, retail and smart-money long/short ratios, liquidation distance, support and resistance, and a one-line context. Fields that were missing at decision time are left out or shown as `-`. |
|
|
| A few rows also end with a desk constraint, for example `[ACCOUNT] Free margin 15.2% (min 30% to open a new position)`. In those rows the correct answer is NO TRADE whatever the signals say. |
|
|
| The assistant turn is a `<think>` trace followed by one decision line: |
|
|
| ```text |
| Decision: SHORT ETH/USDT LIMIT @ 2929.58 at 30x. Setup: breakdown. Conviction: medium. Risk plan: stop 2947, target 2868.77. Key levels: Support 2916.15 | Resistance 2988.23. |
| Decision: NO TRADE. Conviction: low. Key levels: Support 1680.03 | Resistance 1702.88. |
| ``` |
|
|
| ## Configs |
|
|
| ### `sft` |
|
|
| `{"messages": [system, user, assistant]}`. There are 896 examples: 693 winning trades (417 SHORT, 276 LONG) and 203 good skips (NO TRADE). Skips were sampled at 30% of the winner count. Symbols are BTC/USDT 359, ETH/USDT 358, SOL/USDT 113 and AVAX/USDT 66. |
|
|
| ### `kto` |
|
|
| `{"prompt": [system, user], "completion": [assistant], "label": bool}`, the unpaired format used by TRL's `KTOTrainer`. |
|
|
| - **Desirable (`true`), 896 rows:** the same examples as `sft`. |
| - **Undesirable (`false`), 488 rows:** |
| - 458 real **losing trades** (280 SHORT, 178 LONG), held out of SFT |
| - 30 **missed winners**: skips whose counterfactual trade would have reached at least +1R. These are relabelled as undesirable so that skipping a winning setup is penalised. |
|
|
| Counterfactual method: the trade direction is a vote of MACD 4h, RSI 4h and RSI 1h. The stop is the cited support or resistance and the target is ±1.5R, walked forward over a 6-hour horizon and checked against Binance 1-minute prices. If the stop and the target are both hit inside the same candle, it counts as stopped out. The vote under-counts oversold-bounce longs, so 30 is a lower bound. |
|
|
| Qandy 1.0 27B used `undesirable_weight` 1.53 to balance the two classes. |
|
|
| ### `holdout_real` |
| |
| 182 live desk setups that the model never trained on: |
| |
| - `record_type`: `resolved` (83: 52 wins, 31 losses) or `skip` (99, where the desk stood aside) |
| - `output.parsed.decision`: the desk leader's decision |
| - `outcome`: for resolved rows, `status` (win or loss), signed `r_multiple` and an `ambiguous` flag. Empty for skips. |
|
|
| 50 rows carry an older wording of the system prompt, exactly as it was logged at the time. They were kept unchanged so that results stay comparable with the reported evaluation. |
|
|
| ### `backfill_10k` |
| |
| 10,000 replayed desk cycles on BTCUSDT and ETHUSDT (5,000 each), from 2025-12-10 to 2026-06-07. The leader decisions were produced by `claude-opus-4-8`. |
| |
| - `record_type`: `resolved` 1,720, `unfilled` 2,360 (limit order never filled), `skip` 5,920 |
| - `output`: the leader's `decision_line` and `reasoning` |
| - `outcome`: fill and P&L details for resolved rows (`entry`, `fill_time`, `leverage`, `risk_pct`, `reward_pct`, `r_multiple`, `pnl_price_pct`, `pnl_margin_pct`, `fee_margin_pct`), and the intended entry for unfilled rows |
| - Metadata: `idx`, `source`, `symbol`, `cycle_time`, `ref_price`, `kto_label` |
|
|
| These snapshots use an older replay format, so they include Ichimoku and retail long/short fields but no whale flow, and are **not** identical to the live prompt shape. Use `holdout_real` for production-faithful evaluation. |
|
|
| ## Evaluation protocol |
|
|
| Each model's decision on a setup is compared with the leader's trade: |
|
|
| - **Win retention**: share of the leader's winners where the model took the same side |
| - **Loss avoidance**: share of the leader's losers the model skipped or flipped |
| - **Gated sum R**: the leader's P&L, in R multiples, if only the trades the model approved were taken |
|
|
| The scoring script is `scripts/eval_holdout.py` in the [model repo](https://huggingface.co/QandyAI/qandy-1.0-27B). That repo's card has the results. |
|
|
| ## Leakage check |
|
|
| No user snapshot in `holdout_real` or `backfill_10k` appears verbatim in `sft` or `kto`. |
|
|
| ## Limitations |
|
|
| - **Small and narrow.** The data covers about six months (December 2025 to June 2026) of one desk's style: high-leverage scalping on four perpetual pairs, mostly BTC and ETH. It isn't a general market dataset. |
| - **Desk-specific labels.** "Good" and "bad" mean good or bad *for this desk's rules and risk settings*, not universally. |
| - **Sparse signals.** Many snapshots lack several fields, and reasoning traces sometimes lean on the few that are present. |
| - **Small evaluation set.** `holdout_real` has 83 resolved trades, so metrics on it are noisy (roughly ±2–3R). |
| - **Not financial advice.** This data is for research. Don't use models trained on it to place real orders without independent risk controls. |
|
|
| ## About QANDY |
|
|
| QANDY builds personal financial agents. Learn more at [qandy.ai](https://qandy.ai). |
|
|
