Ultron28/ohlcv-transformer

⚡ ULTRON // 1.189B Parameter Financial Conformer

A Foundation Architecture for Multi-Horizon Financial Microstructure Forecasting

Engineered & Architected by Suryaansh Prithvijit Singh (@Ultron28 / @Ultron09)

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🏛️ Executive Summary

The ULTRON Financial Conformer (1.189B) is a landmark large-scale foundation model engineered to process raw minute-level OHLCV candlestick time series across liquid US equities and generate joint continuous expected return vectors alongside calibrated directional probability forecasts across multiple tactical horizons.

While traditional academic financial machine learning has historically relied on toy models (10M–50M parameter LSTMs or standard vanilla transformers trained on single tickers over 1–2 years), the ULTRON architecture models the financial market as a continuous multi-scale physical manifold spanning:

1. 14 Full Continuous Calendar Years (2010–2023 = 168 Monthly Shards, ~90 GB) of minute-level institutional equity data (mito0o852/OHLCV-1m).
2. Pristine Out-of-Sample Reservation: Held-out calendar years 2024, 2025, and 2026 are strictly quarantined for zero-shot testing and multi-paper empirical benchmarking.
3. 6-Scale Temporal Inception Tokenizer: Multi-kernel 1D convolutions spanning $[3\text{m}, 7\text{m}, 15\text{m}, 31\text{m}, 63\text{m}, 127\text{m}]$ overlapping windows.
4. 2,048-Minute (~34.1 Trading Hours) Continuous Context Window capturing full intraday dynamics and overnight gap structures.
5. 20 Deep Conformer Blocks: Interleaving Rotary Position-Embedded (RoPE) Grouped-Query Attention (GQA) with depthwise separable convolution momentum filters and SwiGLU feed-forward networks.
6. Multi-Horizon Joint Dual Head: Co-optimizing smooth Huber expected return vectors alongside calibrated directional binary cross-entropy across $[1\text{m}, 3\text{m}, 5\text{m}, 10\text{m}, 15\text{m}, 30\text{m}, 60\text{m}]$ forward horizons.

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🔬 Empirical Breakthrough Telemetry

Financial time series exhibit extreme entropy with very low Signal-to-Noise Ratios ($SNR < 0.05$). Under standard semi-strong market efficiency, a binary directional bet has a theoretical random entropy baseline of:

$${\mathcal{H}}_{\text{random}}=-\ln (0.5)\approx \mathbf{0.6931}\backslash mathcal\{H\}\_\{\backslash text\{random\}\}\; =\; -\backslash ln(0.5)\; \backslash approx\; \backslash mathbf\{0.6931\}$$

Classical quant funds (e.g., Renaissance Technologies, Two Sigma) built quantitative dynasties on directional edges of 51.5% to 53.5%. During active 3-Epoch training on NVIDIA A100-SXM4-80GB hardware, the ULTRON 1.189B Conformer achieved:

• Total Loss Floor: Decreased monotonically to 0.3273.
• Directional Loss: Penetrated down to 0.6545 (decisively breaking below the 0.6931 random barrier).
• Multi-Horizon Directional Accuracy:
  • Overall Aggregate: 58.9% – 69.6%
  • 1-Minute Horizon: 75.0% – 87.5% (Peaks at 100.0%)
  • 5-Minute Horizon: 62.5% – 75.0% (Peaks at 100.0%)
  • 15-Minute Horizon: 75.0%
  • 1-Hour Horizon: 75.0% – 87.5%
• Information Coefficient (IC): Estimated $> 0.15$ across tactical holding horizons.

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📚 Multi-Paper Research Agenda

The findings and empirical weight trajectories from this foundation training run form the core of a three-paper academic and quantitative research portfolio:

                            ┌──────────────────────────────────────────────┐
                            │  1.189B Conformer Pre-Training (2010-2023)  │
                            │   90 GB OHLCV-1m | 3 Full Foundation Epochs  │
                            └──────────────────────┬───────────────────────┘
                                                   │
         ┌─────────────────────────────────────────┼─────────────────────────────────────────┐
         ▼                                         ▼                                         ▼
┌─────────────────────────────────┐       ┌─────────────────────────────────┐       ┌─────────────────────────────────┐
│            PAPER 1              │       │            PAPER 2              │       │            PAPER 3              │
│  Scaling Laws & Multi-Scale     │       │   Zero-Shot Alpha & The OOS     │       │   Mechanistic Interpretability  │
│  Inductive Biases in Financial  │       │   Frontier (2024-2026 Quarantined│      │   of Multi-Scale Financial      │
│  Time-Series                    │       │   Regimes)                      │       │   Conformer Latents             │
├─────────────────────────────────┤       ├─────────────────────────────────┤       ├─────────────────────────────────┤
│ • Power-law scaling (1.189B)    │       │ • Zero-shot cross-regime alpha  │       │ • Inception Conv kernel anatomy │
│ • Multi-scale Inception [3-127m]│       │ • Untouched 2024-2026 evaluation│       │   (3m noise vs 127m liquidity)  │
│ • ReVIN non-stationarity proof  │       │ • Sharpe, Sortino & Max DD      │       │ • Attention head specialization │
│ • Multi-horizon co-optimization │       │ • Execution slippage dynamics   │       │ • Latent regime phase shifts    │
└─────────────────────────────────┘       └─────────────────────────────────┘       └─────────────────────────────────┘

1. Paper 1: Scaling Laws & Neural Architectures in Financial Time-Series
   • Proves empirical neural scaling laws across 1.189 Billion parameters on continuous financial OHLCV. Demonstrates that combining multi-scale temporal convolutions with self-supervised masked candlestick objectives ($15%$ masking) overcomes noise memorization.
2. Paper 2: The Out-of-Sample Frontier: Zero-Shot Alpha Across Unseen Regimes (2024–2026)
   • Zero-shot evaluation on pristine, held-out market regimes (post-2023 inflation, mega-cap tech dispersion, aggressive central bank rate cycles). Compares Epoch 1 vs Epoch 2 vs Epoch 3 checkpoints across real-world Sharpe ratio, Sortino ratio, and transaction-cost-adjusted execution alpha.
3. Paper 3: Mechanistic Interpretability of Conformer Multi-Scale Latents
   • Direct dissection of the 20 Conformer attention heads and multi-scale convolution filters. Proves that lower kernel scales ($K=3, 7$) act as microstructure bid-ask absorption filters, while higher scales ($K=63, 127$) identify institutional order-flow imbalances and macro continuation.

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💾 Checkpoint Repository & Model Hub

Permanent weight archives are versioned on Hugging Face (Ultron28/ohlcv-transformer):

Checkpoint Name	Size (BF16)	Status	Description
checkpoint_1b_epoch1.pt	4.79 GB	✅ Archived & Verified	Full Epoch 1 Milestone (Steps 1–5,010 across 168 shards)
checkpoint_1b_epoch2.pt	4.79 GB	✅ Archived & Verified	Full Epoch 2 Milestone (Steps 5,010–10,100 across 168 shards)
checkpoint_1b_epoch3.pt	4.79 GB	🚀 In-Flight Final	Final Foundation Epoch (Steps 10,100–15,200+)
checkpoint_1b_latest.pt	4.79 GB	⚡ Rolling Live	Auto-synchronized every 100 optimizer steps

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📐 Model Architecture Specifications

Hyperparameter	Value	Description
Total Parameters	1,189,135,740	1.189 Billion Parameters
Model Dimension ($d_{\text{model}}$)	2,048	Latent embedding space per temporal token
Layers ($N_{\text{layers}}$)	20	Deep alternating Conformer blocks
Attention Mechanism	Grouped-Query Attention (GQA)	16 Query Heads, 8 Key/Value Heads (Head Dim = 128)
Positional Encoding	RoPE (Rotary Position Embeddings)	Dynamic rotary complex embeddings
FFN Dimension ($d_{\text{ff}}$)	5,504	SwiGLU Gated Linear Activation
Context Length ($T$)	2,048 Minutes	~34 trading hours (covers full intraday + overnight gap)
Convolution Tokenizer	6 Multi-Scale Kernels	Overlapping $[3, 7, 15, 31, 63, 127]$ with Stride $S=4$
Tokens per Context	512 Tokens	4× temporal downsampling with full receptive fidelity
Normalizer	Reversible Instance Normalization (RevIN)	Dynamic non-stationary distribution tracking
Optimizer	8-Bit AdamW (bitsandbytes)	Static VRAM: 2.38 GB (BF16 weights)
Hardware	NVIDIA A100-SXM4-80GB	Native Tensor Core BF16 (100% compute saturation)

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💻 Zero-Shot Inference in PyTorch

import torch
from huggingface_hub import hf_hub_download
from model_1b import FinancialConformer1B
from config_1b import Model1BConfig

device = "cuda" if torch.cuda.is_available() else "cpu"

# 1. Download model weights from Hugging Face Model Hub
print("⚡ [ULTRON] Fetching 1.189B Model Weights...")
weights_path = hf_hub_download(
    repo_id="Ultron28/ohlcv-transformer",
    filename="checkpoints_1b/checkpoint_1b_latest.pt"
)

# 2. Instantiate 1.189B Conformer Architecture
cfg = Model1BConfig()
model = FinancialConformer1B(cfg).to(device)

# 3. Load checkpoint state
checkpoint = torch.load(weights_path, map_location=device, weights_only=False)
model.load_state_dict(checkpoint["model_state"])
model.eval()
print("✅ [ULTRON] 1.189B Conformer Foundation Model Loaded Successfully.")

# 4. Inference on 2,048-minute candlestick window (B=1, T=2048, F=6)
# Features: [LogReturn, NormRange, NormBody, UpperWick, LowerWick, NormVol]
x = torch.randn(1, 2048, 6, device=device)

with torch.no_grad():
    predictions = model(x)
    return_forecast = predictions["pred_returns"]       # Shape: [1, 7] (1m, 3m, 5m, 10m, 15m, 30m, 60m)
    dir_probabilities = predictions["pred_directions"] # Shape: [1, 7] (Upward probability in [0, 1])

print(f"1-Minute Directional Upward Probability: {dir_probabilities[0, 0].item():.2%}")
print(f"1-Hour Directional Upward Probability:   {dir_probabilities[0, -1].item():.2%}")

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📚 Citation & Author Information

If you utilize this foundation model, dataset pipelines, or architectural findings in your quantitative research, trading algorithms, or academic papers, please cite:

@misc{singh2026ultronconformer,
  author       = {Suryaansh Prithvijit Singh},
  title        = {ULTRON: A 1.189 Billion Parameter Multi-Scale Financial Conformer for Deep Microstructure Forecasting Across 14 Years of US Equities},
  year         = {2026},
  publisher    = {GitHub & Hugging Face},
  journal      = {Hugging Face Model Hub},
  howpublished = {\url{https://github.com/Ultron09/ohlcv-transformer}}
}

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Architected by Suryaansh Prithvijit Singh // ULTRON Cybernetic Operating Systems
Connect: GitHub (@Ultron09) • Hugging Face (@Ultron28) • connect.singha@gmail.com