AI-powered forecastsConfidence scoringTransparent backtests

AI-powered market forecasts with calibrated confidence

Nordict uses multiple AI models trained on historical market data to generate probabilistic price forecasts across multiple time horizons, tracked and evaluated with walk-forward backtesting.

Get startedView methodology

Supported markets: Crypto • Indices (coming) • FX (coming)

ML-driven models

Trained on historical data and evaluated out-of-sample.

Confidence bands

Uncertainty is explicit, not hidden.

Walk-forward evaluation

Backtests designed to avoid leakage.

Supported markets

BitcoinBTC
Ξ
EthereumETH
BNB
BNBBNB
SolanaSOL
XRP
XRPXRP
ADA
CardanoADA
Ð
DogecoinDOGE
AVAX
AvalancheAVAX
DOT
PolkadotDOT
LINK
ChainlinkLINK
SHIB
Shiba InuSHIB
Ł
LitecoinLTC
UNI
UniswapUNI
ATOM
CosmosATOM
XLM
StellarXLM
ETC
Ethereum ClassicETC
FIL
FilecoinFIL
NEAR
NEAR ProtocolNEAR
APT
AptosAPT
ARB
ArbitrumARB
OP
OptimismOP
INJ
InjectiveINJ
SUI
SuiSUI
SEI
SeiSEI
TIA
CelestiaTIA
RNDR
RenderRENDER
FET
Fetch.aiFET
TAO
BittensorTAO
WIF
dogwifhatWIF
PEPE
PepePEPE
IMX
ImmutableIMX
STX
StacksSTX
MKR
MakerMKR
AAVE
AaveAAVE
GRT
The GraphGRT
SNX
SynthetixSNX
LDO
Lido DAOLDO
CRV
CurveCRV
RUNE
THORChainRUNE
ENS
ENSENS
SAND
The SandboxSAND
MANA
DecentralandMANA
AXS
Axie InfinityAXS
GALA
GalaGALA
FLOW
FlowFLOW
CHZ
ChilizCHZ
ENJ
Enjin CoinENJ
GMT
STEPNGMT
APE
ApeCoinAPE
ALGO
AlgorandALGO
S&P
S&P 500SPXsoon
NDX
NASDAQNDXsoon
DJI
Dow JonesDJIsoon
€/$
EUR/USDEURUSDsoon
£/$
GBP/USDGBPUSDsoon
$/¥
USD/JPYUSDJPYsoon

More assets and FX pairs coming soon.

Forecast + confidence bands

Rigor you can verify

Built for transparent evaluation, not hype.

Forecasts are only useful when uncertainty is measurable.

Nordict emphasizes validation, calibration, and traceability so you can interpret signals with context, and audit results over time.

Walk-forward backtests

Designed to reduce leakage

Calibrated confidence

Bands mean something

Model versioning

Compare releases over time

Transparent metrics

Performance you can verify

Problem → Solution

Markets are noisy

Point predictions hide uncertainty. Backtests are often optimistic. Performance is rarely tracked once models go live.

  • Overfitting disguised as accuracy
  • No visibility into uncertainty
  • Backtests that don't reflect live conditions

We model uncertainty explicitly

Nordict produces probabilistic forecasts across multiple horizons and tracks performance using walk-forward evaluation.

  • Confidence bands instead of single outcomes
  • Multi-horizon forecasts for context
  • Versioned models with live performance tracking

Core features

Everything is built around confidence and verification.

Forecasts are paired with uncertainty, performance tracking, and an auditable model lifecycle, so signals stay accountable.

Forecasts

Multi-horizon forecasts

Switch between intraday and longer horizons to match your decision window.

Confidence

Confidence scoring

Calibrated bands and probabilities so uncertainty is explicit and usable.

Performance

Backtesting & performance

Walk-forward evaluation with metrics you can audit over time.

Alerts

Alerts & signals

Get notified on threshold moves, regime shifts, or confidence changes.

APIComing soon

API access

Programmatic access to forecasts, history, and model metadata (coming soon).

Product preview

See forecasts, confidence, and performance in one place.

Interactive panels let you switch horizons, inspect uncertainty, and track how models perform over time.

Forecast panel
Performance panel
Explore product

All visuals shown use illustrative data.

How it works

A simple workflow, designed for real-world evaluation.

Nordict separates ingestion, training, and serving so the web app stays fast while model workloads scale independently.

01

Ingest & normalize data

Fetch market data, validate inputs, and store clean time-series for training and evaluation.

02

Train & version models

Run walk-forward training and evaluation, store model artifacts, and record metadata for auditability.

03

Serve forecasts & track performance

Generate multi-horizon predictions with confidence scoring and continuously monitor live results.

Methodology highlights

Designed for statistical rigor and transparency.

Forecasts are only as useful as the evaluation behind them. Each model is trained, validated, and monitored with explicit safeguards.

Walk-forward validation

Models are evaluated sequentially to better reflect live deployment conditions.

Leakage prevention

Strict separation of training, validation, and test windows.

Confidence calibration

Probabilities are evaluated so confidence bands remain meaningful.

Regime-aware evaluation

Performance is inspected across different market conditions.

Use cases

Built for different decision styles.

The same forecasting engine adapts to how individuals and teams make decisions without changing the underlying rigor.

Traders

Use short- and medium-horizon forecasts with confidence bands to improve timing and manage risk.

  • Intraday to multi-day horizons
  • Confidence-aware entries & exits
  • Alert-driven monitoring

Investors

Frame longer-term direction and downside risk with probabilistic scenarios and regime context.

  • Weekly horizons & trend context
  • Downside-aware confidence
  • Backtested performance views

Teams

Coming soon

Share forecasts, track versions, and integrate via API for research and production workflows.

  • Shared dashboards
  • Model version comparisons
  • API & exports (coming)

FAQ

Common questions, answered clearly.

Transparency matters. If you have additional questions, feel free to reach out.