AgenticInvestor

EUR/USD

Foreign exchange · Monthly series · FX-EURUSD

Latest, October 2026

1 USD

-1.5% from September 2026

Open interactive chartEUR/USD in the app

1.2441.1120.98November 2016October 2027
History through October 2026, the forecast path to October 2027 (dashed) and its 80% interval (shaded).

Forecast

Forecast for October 2027: 1 USD, with an 80% interval of 1 USD to 1 USD.

Forecast path. Intervals are shown where available.
PeriodForecast80% low80% high
November 20261 USD1 USD1 USD
December 20261 USD1 USD1 USD
January 20271 USD1 USD1 USD
February 20271 USD1 USD1 USD
March 20271 USD1 USD1 USD
April 20271 USD1 USD1 USD
May 20271 USD1 USD1 USD
June 20271 USD1 USD1 USD
July 20271 USD1 USD1 USD
August 20271 USD1 USD1 USD
September 20271 USD1 USD1 USD
October 20271 USD1 USD1 USD

Model: Agentic Investor TSFM (foundation model). Forecast generated 2026-11-14.

AI confidence

Mixed evidence

The tests show no consistent, material advantage over the no-change baseline.

Model: Agentic Investor TSFM (foundation model)

Accuracy vs naive
-3%Matches the no-change baseline over 6 tests (6 tests, 2 won)
Direction
40%No clear edge over a coin flip (29 of 72 calls right in 6 tests)
Turning points
13 of 30Anticipated some turns (30 turns in 6 tests, 0 without a turn)
Error vs baseline
3% higherError ratio 1.03; 1.00 is a tie.
Tests won
2 / 6Complete recent tests excluded from model selection; ties count as no win.
Test period
2020-11-01 to 2026-10-0112 observations ahead per test.
80% band coverage
83%Observed outcomes inside the band across all stored test windows.
How this assessment is made

AI confidence describes historical evidence for this forecast, not a probability of being correct. Each backtest fits on data available before its test window. The baseline carries the last value forward. Supported evidence requires at least six complete, non-overlapping tests, an error ratio below 0.90 (rounded to two decimals), and wins in a majority of tests. Ratios from 0.90 to below 1.10 are on par; 1.10 or above is weak evidence. Inconsistent wins produce mixed evidence. For a selected model, only the recent tests excluded from model selection determine this label. These are historical simulations using the available data, not a live trading track record; revisions and foundation-model pretraining can limit how closely they reproduce a past decision. Past performance can change.

Replay past tests

Model comparison

4 of 5 models evaluatedModels are selected on older tests and evaluated on separate recent tests. Some candidates failed or returned incomplete tests; the table shows which. An eligible nightly batch can compare four classes, subject to call budgets and retry limits.

Models tested

Agentic Investor TSFM scored lowest on the 6 older tests (1.00) and was chosen. The 6 recent tests played no part in the choice and give its grade (1.03). The search for a model that beats the naive forecast on both sets of tests goes on. Scores are MASE: a model's errors divided by the naive forecast's, so 1 is a tie.

4 model classes completed the tests: Foundation model, Statistical, Machine learning, Neural network.

Every model tested on this series.
ModelClass and computeOlder testsRecent testsRole
Naive forecast (last value held)Benchmark · CPU1.001.00Benchmark
Agentic Investor TSFM (foundation model)Foundation model · CPU1.001.03Serving
Exponential smoothing (statistical)Statistical · CPU1.091.04Tested
Gradient-boosted trees (machine learning)Machine learning · CPU1.730.98Tested
Hierarchical neural network (neural network)Neural network · CPU1.731.27Tested
State-space model, 167M (foundation model)Foundation model · CPUn/an/aDid not run

Uses ci-proto, released by Continuous Intelligence. Continuous Intelligence does not endorse or approve this product or service. (ci-proto is shown as State-space model, 167M.)

Reading

It is above 45% of the 119 earlier readings since November 2016.

Written from the figures by a fixed template; no AI model is involved.

Recent history (24 observations)
The 24 most recent observations, newest first.
PeriodValue
October 20261 USD
September 20261 USD
August 20261 USD
July 20261 USD
June 20261 USD
May 20261 USD
April 20261 USD
March 20261 USD
February 20261 USD
January 20261 USD
December 20251 USD
November 20251 USD
October 20251 USD
September 20251 USD
August 20251 USD
July 20251 USD
June 20251 USD
May 20251 USD
April 20251 USD
March 20251 USD
February 20251 USD
January 20251 USD
December 20241 USD
November 20241 USD

Source

Data: public market data feeds.

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