InsightAide

// Route each request to the right provider by model prefix,
// then record token usage + estimated cost for every call.
public async Task<LlmResponse> CompleteAsync(LlmRequest request, CancellationToken ct)
{
    ILlmProvider provider = request.Model switch
    {
        var m when m.StartsWith("claude-") => _anthropic,
        var m when m.StartsWith("gpt-")    => _openAi,
        _                                  => _default,
    };

    var response = await provider.CompleteAsync(request, ct);

    await _usageLog.RecordAsync(new ApiUsage(
        provider:         provider.Name,
        model:            request.Model,
        inputTokens:      response.Usage.InputTokens,
        outputTokens:     response.Usage.OutputTokens,
        estimatedCostUsd: _pricing.Estimate(request.Model, response.Usage)), ct);

    return response;
}

// Drop any training row whose label window overlaps the test fold,
// plus an embargo buffer on each side.
private IReadOnlyList<Sample> PurgeAndEmbargo(
    IReadOnlyList<Sample> train,
    DateRange testFold,
    TimeSpan labelWindow,
    TimeSpan embargo)
{
    var lower = testFold.Start - labelWindow - embargo;
    var upper = testFold.End + embargo;

    return train
        .Where(s => s.Timestamp < lower || s.Timestamp > upper)
        .ToList();
}

// Persist a model only if its out-of-sample top-vs-bottom spread
// is statistically significant — not merely positive.
var spread = oos.TopDecileReturn - oos.BottomDecileReturn;
var tStat  = spread.Mean / spread.StdError;

if (tStat < MinTStat)   // MinTStat = 1.0
{
    _log.LogInformation(
        "Rejecting model: OOS spread t-stat {T:F2} below gate", tStat);
    return SaveResult.Rejected;
}

await _registry.SaveAsync(model, metrics, ct);