kronos-mql5

A native MQL5 port of Kronos-small inference. Kronos is a pretrained transformer that forecasts candlesticks the way a language model predicts words.

What it does

The whole forward pass runs inside MetaTrader 5. There is no Python at runtime. Python is used once, offline, to export the weights and to capture reference activations for verification.

The port is hand-written in MQL5 matrix and vector operations: RMSNorm, SwiGLU, rotary position embeddings, scaled dot-product attention, causal multi-head attention and non-causal cross-attention, plus the BSQ tokenizer math that turns OHLCV bars into tokens and back.

Every stage has its own verification harness that checks MQL5 output against the captured PyTorch reference. That is the part worth attention: a transformer with a transposed weight or an off-by-one in the KV cache still produces confident-looking forecasts.

On top of that sits a second, separate question: whether the forecasts are any good. Verification answers fidelity to PyTorch and nothing else. The evaluation harness answers market value, and the two are not the same claim.

Layout

Include/Kronos/KronosTokenizerMath.mqh    preprocessing, BSQ bit math, weight loading
Include/Kronos/KronosTransformerCore.mqh  RMSNorm, SwiGLU, RoPE, attention, blocks
Include/Kronos/KronosSampling.mqh         softmax, top-k and top-p, multinomial
Include/Kronos/KronosEncoder.mqh          tokenizer encode chain
Include/Kronos/KronosDecoder.mqh          tokenizer decode chain
Include/Kronos/KronosPredictorS1.mqh      first-stage decode with KV cache
Include/Kronos/KronosPredictorS2.mqh      second-stage decode with cross-attention
Include/Kronos/KronosInference.mqh        autoregressive loop
Experts/Kronos/KronosForecast.mq5         draws the forecast candles on a live chart
Experts/Kronos/KronosSignalEA.mq5         Strategy Tester rule over a precomputed dump
Scripts/Kronos/KronosSelfTests.mq5        weight-free unit checks
Scripts/Kronos/KronosVerify*.mq5          per-stage checks against the reference
Scripts/Kronos/KronosBench.mq5            timing
Scripts/Kronos/KronosProfile.mq5          profiling
Scripts/Kronos/KronosForecastDump.mq5     walk-forward forecast dump to CSV
Scripts/Kronos/KronosEvalQuality.mq5      forecast quality against a random walk
Kronos_Python/export_kronos_weights.py    one-off weight export
Kronos_Python/kronos_reference_capture.py reference activations for verification

Running it

1. Export weights with export_kronos_weights.py and capture references with kronos_reference_capture.py. Both are offline, run once.
2. Run KronosSelfTests.mq5, then the KronosVerify* harnesses in order. Each checks one stage against the reference.
3. Only then run inference. KronosForecast.mq5 attaches to a chart and draws the predicted candles to the right of live price.

Evaluating it

The evaluation is deliberately split into three programs so that no analysis can change what the model predicted, and so that look-ahead is structurally hard rather than merely promised.

1. KronosForecastDump.mq5 walks history once and writes greedy forecasts, plus the realized future closes, to CSV. Context uses only bars before the evaluation bar; the realized closes are stored for scoring and never feed a decision. Greedy decoding makes the whole study reproduce exactly.
2. KronosEvalQuality.mq5 scores that dump per horizon: directional accuracy, and an error skill ratio against a random-walk (no-change) baseline. A ratio below 1.0 means the model beats the baseline.
3. KronosSignalEA.mq5 runs a deliberately trivial threshold rule over the same dump in the Strategy Tester. It performs no inference and discards the realized-close columns as it parses.

The dump is written to the terminal's shared Common\Files folder rather than MQL5\Files, because a Strategy Tester agent is sandboxed to its own private Files directory and cannot read what the script wrote otherwise. All three programs open the CSV with FILE_COMMON.

Result

On EURUSD H1 with tick-derived volume, this model has no usable forecasting edge. Directional accuracy is 51% across roughly 15,000 horizon samples, and the error skill ratio is above 1.0 at every horizon, meaning the point forecasts are less accurate than assuming price will not move. A fair backtest at 150 trades gives Profit Factor 1.02 and Sharpe 0.13, before any spread or commission.

A high signal threshold produces a far prettier report, Profit Factor 2.70 and Sharpe 1.87, but from only 15 trades, with a Z-Score confidence of 34%: it is statistically indistinguishable from random. That contrast is the most useful thing in this repository. Read the trade count before believing an equity curve.

Likely reasons for the negative result: Kronos was trained on equities and crypto on daily and longer bars, FX feeds carry no real exchange volume so two of the six input features are proxies the model never saw in training, and EURUSD intraday is close to a martingale where "no change" is a very strong baseline. A retest on daily bars with real volume is the obvious follow-up, and it belongs alongside this result rather than instead of it.

Disclaimer

Educational code. Past behaviour of any model or dataset says nothing about future results. Test on your own data and broker conditions before drawing conclusions.