Adds the two sweep axes that the spec's deployability grid needs but
were missing from the kernels:
Threshold gate (decision_policy.cu, both kernels):
- New per-backtest `threshold_per_b` array kernel arg.
- Pre-Kelly prelude: if max_h |alpha[h] - 0.5| * 2 < threshold[b],
emit noop and return. Kept deterministic from alpha alone so the
threshold pre-registration step (p60-p95 absolute calibration on a
validation window, future P6) reflects exactly what gets gated in
deployment.
Per-fill cost integration (resting_orders.cu / apply_fill_to_pos):
- apply_fill_to_pos signature grows three args: b, cost_per_lot_per_side_per_b,
total_fees_per_b. Single insertion point at line 90.
- After the close-leg realized_pnl math runs (so the gross unwind P&L
is preserved), deduct fill_cost = filled_lots * cost_per_lot_per_side[b]
from pos.realized_pnl AND accumulate into total_fees_per_b[b].
- Net-of-cost semantics: isv_kelly_update_on_close reads realized_pnl
delta which is now net of cost — Kelly state learns from realistic
return distribution.
- All 3 apply_fill_to_pos call sites in step_resting_orders updated.
order_match.cu's submit_market_immediate path is dead code in the
post-P1 flow (everything routes through seed_inflight_limits_batched
→ step_resting_orders → apply_fill_to_pos) so not touched here.
BatchedSimConfig + UniformSimParams + BacktestHarnessConfig gain
threshold + cost_per_lot_per_side fields. All UniformSimParams
constructors in tests and main.rs updated with defaults (0.0, 0.0 =
gate disabled, frictionless).
Regression:
- threshold_gate_skips_low_conviction (p=0.51 + threshold=0.10 → noop)
- threshold_gate_allows_high_conviction (p=0.8 + threshold=0.10 → buy 1+)
- threshold_zero_is_passthrough (sanity)
- All P1+P2+P3 tests continue to pass via the new ABI.
cost_deducted_at_each_fill + kelly_state_sees_net_return end-to-end
tests deferred — they require a full submit_market → fill → close
sequence, which the production smoke exercises.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Production smoke completed end-to-end but produced n_trades=0 across 99,969
decisions — `decision_policy_default` and `decision_policy_program` both
applied a sentinel-skip pattern: if `isv_kelly_d` had not been seeded
(pnl_ema_win == 0), each horizon's signed-size stayed zero, AND each
horizon's aggregation weight (= recent_sharpe) also stayed zero. The
cross-horizon w_sum was therefore 0, final_size was 0, every market_target
was noop. State only updates on trade close → no trade ever fires →
infinite cold-start.
Per pearl_blend_formulas_must_have_permanent_floor (`max(real, floor)`,
not blend) and pearl_kelly_cap_signal_driven_floors, replace the sentinel-
skip with a two-layer floor on each kernel:
1. Kelly fraction: `max(kelly_frac_floor, computed_kelly)` — when state
is sentinel, falls back to the floor directly. Cap_lots falls back
to `max_lots` when realised_return_var is sentinel.
2. Aggregation weight: `max(sharpe_weight_floor, recent_sharpe)` — lets
cross-horizon sum produce a non-zero size before recent_sharpe is
populated. Once a horizon shows positive sharpe it dominates.
Plumbed through `step_decision_with_latency` / `step_decision` as two
new f32 args (atomic contract change, every caller migrated). Defaults
0.20 / 0.10 chosen so a strong-conviction signal (sig_mag ≥ 0.5) fires
1 lot at cold-start under max_lots=5 while weaker signals stay flat
(see `default_kelly_frac_floor` comment for the arithmetic). Exposed
as CLI flags + sweep-grid base/cell overrides.
Regression test `decision_floor_coldstart` proves:
- default floors (0.20/0.10) fire a 1-lot buy with p_h=0.8 and zero state
- zero floors reproduce the original noop bug
Also moves `aggregate` step to the GPU pool because fxt-backtest is
dynamically linked against libcuda.so.1 (the ci-compile-cpu hosts
don't expose CUDA driver libs).
Verified locally on RTX 3050 Ti — workspace cargo check passes, both
regression tests pass, trunk save/load roundtrip still passes.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Adds `fxt-backtest verdict <sweep_dir> --threshold X --windows W1,W2,W3,W4
--training-sha SHA --spec-sha SHA --out deployability_verdict.json`
that reads per-cell summary.json files at the realistic (1 tick, 200ms)
+ stress (1.5 tick, 400ms) anchors against the pre-registered threshold,
computes median Sharpe/max_dd/Sortino/profit_factor across windows,
classifies into Pass-robust / Pass-nominal / Fail-inconclusive / Fail /
Fail-degenerate per spec §3.5, and writes the audit JSON.
Adds serde_json workspace dep to fxt-backtest's Cargo.toml (was already
a transitive dep but not declared at this layer).
End-to-end CLI for Phase 2 runtime is now: argo-train.sh → argo-lob-sweep.sh
(smoke / threshold-tuning / deployability) → fxt-backtest aggregate →
fxt-backtest verdict → commit deployability_verdict.json.
BacktestHarness now owns a PerceptionTrainer (in inference role) instead
of a raw CfcTrunk. The sliding K-window of recent snapshots accumulates
in the harness; at each decision-stride boundary (and only once the
window has reached cfg.seq_len), the harness calls
trainer.forward_only(&window) and broadcasts the last K position's
per-horizon probs to the LobSim.
fxt-backtest's main.rs constructs the trainer via
PerceptionTrainer::from_checkpoint when --checkpoint is supplied (else
random init for noise baseline).
Why this shape: PerceptionTrainer's evaluate_batched already runs the
full inference chain (snap → vsn → mamba2 → ln → mamba2 → ln →
attn_pool → cfc K-loop → grn heads) correctly. Duplicating that 400-line
forward chain on CfcTrunk would double the surface area for the same
result — the trunk's role is weight-source-of-truth (achieved in X1-X9),
not kernel-launch orchestration.
End-to-end status: alpha_train emits Checkpoint files via X14 wiring;
fxt-backtest now loads those Checkpoints via from_checkpoint and drives
forward via forward_only. Phase 2 (Argo runtime: training → smoke →
threshold pre-reg → 560-cell deployability sweep → verdict) is unblocked.
Adds PerceptionTrainer::config() accessor so the harness can read seq_len.
Verification: ml-alpha + ml-backtesting + fxt-backtest all build clean.
New `fxt-backtest sweep --grid <yaml> --out <dir>` subcommand: iterates
over a grid of Run configs, writes each cell's artifacts to
<out>/<cell_name>/, then automatically invokes the existing aggregate
path to produce aggregate.parquet + pareto_frontier.json at the root.
All cells run sequentially on the same GPU (single-machine). For
cluster fan-out the underlying mechanism is the same — Argo can wrap
this binary in a workflow that runs each cell as a separate pod
(left as infra-side work for a follow-up commit; the binary's
contract is the same).
Sweep grid YAML schema:
base: # defaults applied to every cell unless overridden
data: ...
n_parallel: ...
decision_stride: ...
latency_ns: ...
target_annual_vol_units: ...
annualisation_factor: ...
max_lots: ...
max_events: ...
seed: ...
checkpoint: ... # optional — load real trained weights
cells:
- name: cell_a
decision_stride: 1 # override base
- name: cell_b
latency_ns: 250000000
...
Each SweepCell may override any subset of fields; unset fields fall
back to base defaults. --max-cells gates the run for smoke-testing
large grids.
Adds an example grid at config/ml/sweep_decision_stride_example.yaml
that re-runs the decision_stride ∈ {1, 2, 4, 8} sweep deferred from
the original plan (task #202).
Closes#201 (sweep tool). #202 (first decision_stride sweep) is now
executable end-to-end via the example grid.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
CfcTrunk::save_checkpoint(path) reads each device weight tensor back
via memcpy_dtoh and bincode-serialises into a CheckpointV1 envelope:
{ version, n_in, n_hid,
w_in, w_rec, b, tau,
heads_w, heads_b,
proj_w, proj_b, proj_g, proj_n }
Total ~22k-25k f32 = ~90 KB per trunk. Tiny.
CfcTrunk::load_checkpoint(dev, cfg, path) deserialises + validates
(version == 1, n_in/n_hid match the supplied CfcConfig — a model
trained for one arch can't silently load against another). Constructs
a fresh trunk via new_random (for kernel bindings + scratch buffers)
then overwrites every weight tensor via memcpy_htod. The random init
values are thrown away — marginally wasteful, but keeps the
construction code paths unified.
Roundtrip test (--ignored, CUDA-required): save trunk_A → load → read
back every device tensor and assert bit-equality between trunk_A and
the loaded trunk_B. Passed locally. Dim-mismatch rejection test runs
without CUDA (verifies bincode envelope serialise/deserialise).
bin/fxt-backtest --checkpoint <path>: when set, overrides --seed and
loads from disk. When absent, warns loudly that the trunk is
random-initialised and backtest results are noise. This makes the
binary genuinely useful as a deployment tool — point it at a trained
checkpoint and run real backtests.
Adds bincode workspace dep to ml-alpha (was already in workspace
dependencies, just not in ml-alpha's [dependencies] block). serde
features bumped to ["derive"] (was using workspace default which
omits derive macros).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
The hardcoded WeightedByRealizedSharpe path from C7 is now joined by
a stack-based bytecode interpreter that consumes Strategy::flatten()
output, unlocking RegimeSwitch / Portfolio / non-default Ensemble /
single-Leaf compositions specified via policy-grid YAML.
cuda/decision_policy.cu — new kernel `decision_policy_program`:
Stack-based VM with parallel (value, attribution_mask) stacks.
Opcodes mirror src/policy/mod.rs::OpCode exactly:
NoOp / PushScalar / EvalRegime / BranchIfRegime
EmitPerHorizonSize (computes sized intent from alpha[h] +
IsvKellyState[h] using same Kelly + ISV-cap formula as the
hardcoded default)
AggMean / AggWeightedSharpe / AggMaxConfidence (pop n values,
push aggregated, OR attribution masks)
ApplyConflict (v1 no-op, reserved for Portfolio)
WriteOrder (terminal — converts top-of-stack to market_target +
open_horizon_masks attribution if currently flat)
AggWeightedSharpe recovers the source horizon from a single-bit
attribution mask to look up recent_sharpe; multi-bit masks (nested
aggregators that collapsed horizons) fall back to uniform weight.
decision_policy_default extended with a program_lens param: skips any
backtest whose plen > 0 (the program kernel handled it). The two
kernels run sequentially in step_decision_with_latency with mutual
exclusivity on each backtest slot.
LobSimCuda gains:
upload_program(b, &Program) — uploads a Strategy::flatten() output
to backtest b's slot in program_table_d, updates program_lens_d.
set_regime(b, regime_id) — writes regimes_d for OP_EVAL_REGIME /
OP_BRANCH_IF_REGIME consumption.
BacktestHarnessConfig.strategies (Vec<Strategy>) — empty means every
cell uses the hardcoded default; non-empty len must equal n_parallel
and each strategy is flattened + uploaded at construction.
bin/fxt-backtest --policy-grid <yaml> path now actually plumbs through
to the kernel (was parsed-but-ignored in C9). Empty grid keeps the
default behaviour.
New fixture decision_program_h4_only: uploads Strategy::Leaf(h4_only)
flattened to (EmitPerHorizonSize, WriteOrder) — 2 instructions, 16
bytes — and verifies the bytecode kernel produces equivalent end-state
to the hardcoded default (3 lot buy at vwap=5500). Proves the VM
dispatcher works end-to-end.
12/12 GPU fixtures green. 33 lib unit tests + 3 Ring 2 fuzz tests
still green.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
The in-flight machinery from C11 is now reachable from the harness +
CLI. New step_decision_with_latency on LobSimCuda:
latency_ns == 0 → existing immediate-match path (submit_market_immediate
kernel fills against current book).
latency_ns > 0 → host reads market_targets, converts each non-noop
into seed_limit_order(active=2, price=very-aggressive,
arrival_ts_ns = current + latency_ns). The
resting_orders kernel promotes + fills at arrival
time, so the order sees whatever book exists then —
not the book at decision time.
Aggressive-price heuristic: buy at 1e9 / sell at 0 — the marketability
check (price ≥ best ask for buy, ≤ best bid for sell) unconditionally
crosses at arrival; the kernel then walks book levels for the actual
fill price. This models "market order with latency" correctly because
slippage emerges from the book-walking at arrival, not from a limit
price boundary.
BacktestHarness gains a latency_ns field; harness::run() always calls
step_decision_with_latency now. Also calls sim.step_resting_orders
per event (with trade_signed_vol=0.0 — the Mbp10RawInput trade-flow
hookup is deferred to a trades-feed integration commit) so in-flight
orders get a chance to promote on every snapshot.
bin/fxt-backtest no longer ignores --latency-ns; the flag value
propagates through BacktestHarnessConfig.latency_ns into the kernel
path. Default stays at 100_000_000 (IBKR + Scaleway baseline per
spec §4). Setting --latency-ns 0 selects the legacy immediate path.
Adds latency_in_flight_miss GPU fixture: limit at 5510 submitted
active=2 at T=1s with arrival_ts=T+100ms. step_resting at T+50ms
sees no promotion (still in-flight). Book moves +5 against buyer
during the 100ms window. step_resting at T+150ms promotes the
limit and fills it at the WORSE post-move book (vwap=5505 vs the
original 5500 it would have hit without latency). Slot ends active=0.
11/11 GPU fixtures green on RTX 3050. 33 lib tests still green.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
artifacts.rs:
- Summary struct (total_pnl_usd, sharpe_ann, sortino_ann,
max_drawdown_usd, calmar, n_trades, win_rate, avg_win/avg_loss,
profit_factor, total_fees_usd, exposure_pct,
kelly_cap_history_sample).
- compute_summary(records, pnl_curve_usd) — non-overlapping
annualisation × √825 per pearl_phase1d4_backtest_cost_edge_frontier
(K=6000 holding × 250 trading days ≈ 825 trades/year).
Sharpe + Sortino + max drawdown + Calmar.
- write_summary (JSON pretty-printed), write_trades_csv (with USD
conversion from fp ×100), write_pnl_curve_bin (bytemuck-cast f32
slice). 5 unit tests with tempdir.
aggregate.rs:
- aggregate_sweep_dir walks <root>/<cell>/summary.json, builds an
arrow RecordBatch (cell name + 9 stats columns), writes
SNAPPY-compressed aggregate.parquet.
- pareto_frontier: cells are kept unless another cell weakly
dominates on all three of (sharpe_ann maxed, max_drawdown_usd
minimised, total_fees_usd minimised) AND strictly improves on one.
Written to pareto_frontier.json (Vec<cell-name>).
- 2 unit tests (3-cell mutual-non-dominance; B-dominates-A).
harness.rs:
- run() now samples Pos.realized_pnl × $50/index-pt per event into
self.pnl_curves[b], so the per-cell P&L curve is ready for
write_artifacts() without an extra sim pass.
- write_artifacts(out_dir) — per-cell <out>/cell_NNNN/{summary.json,
trades.csv, pnl_curve.bin}.
bin/fxt-backtest:
- clap-derive CLI with two subcommands:
run --data <dir> [--predecoded-dir <dir>] [--policy-grid <yaml>]
[--n-parallel N] [--decision-stride S] [--latency-ns N]
[--target-annual-vol-units F] [--annualisation-factor F]
[--max-lots N] [--max-events N] [--seed N] --out <dir>
aggregate <sweep_dir>
- Constructs MlDevice::cuda(0) + CfcTrunk::new_random for the trunk
(v1 — ml-alpha has no checkpoint format yet; --seed gates init).
- Parses --policy-grid YAML if given but doesn't yet plumb to the
LobSimCuda decision kernel (the v1 kernel hardcodes the
Strategy::default_for path; bytecode VM is C7's deferred follow-up).
Parse step kept end-to-end so the YAML format is validated now.
- --latency-ns parsed but not consumed — reserved for follow-up
resting-order in-flight promotion (deferred from C5).
Adds parquet + arrow + arrow-array + arrow-schema + serde_yaml to
ml-backtesting deps; bin/fxt-backtest added to workspace members.
All 33 lib tests + 6 GPU fixture tests green. CLI --help renders both
subcommands correctly.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
client_performance.rs, configuration_benchmarks.rs and
serialization_benchmarks.rs were wholly commented-out /* ... */
bodies with a `fn main()` placeholder and a TODO explaining that
either the types or the crate deps they referenced never existed.
They never produced measurements and benchmarks do not ship, so
delete them and drop their `[[bench]]` entries from Cargo.toml.
The remaining `encryption_performance` bench (auth token storage)
is kept as-is.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The non-interactive `login_with_credentials` path already speaks to
`AuthServiceClient` via gRPC. The interactive login / MFA / refresh
paths still return simulated responses — reword the inline comments
and the `tracing::debug!` messages to describe that split plainly
rather than labelling the gRPC-less paths as TODOs. The SECURITY
warn!() lines are untouched so the runtime signal remains.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
1. fxt MCP server tests: set XDG_CONFIG_HOME to writable temp dir
in test helper. On CI, $HOME=/root/ but pod runs as uid 1000,
so FileTokenStorage::new() fails reading /root/.config/.
2. risk test_hf_gate_check: remove sub-100μs latency assertion
(correctness test, not benchmark — flaky under CPU contention).
3. ml-labeling fractional_diff: remove sub-1μs latency assertions
from correctness tests (latency benchmark is already #[ignore]d).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Replace simulated login in fxt CLI with real AuthServiceClient gRPC call
- Add auth proto to fxt build.rs compile list and lib.rs proto module
- Add api.access permission to JWT claims issued by AuthGrpcService
- Remove orphaned ci-sensor.yaml (replaced by cluster-applied version)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- JWT issuer now foxhunt-api across all 16 files (services, tests, config, docker-compose)
- Remove serde alias api_gateway_url from FxtConfig (no backwards compat)
- Remove api_gateway CLI alias from e2e orchestrator
- All services must deploy simultaneously for JWT validation to match
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add live training metrics monitor CLI command (streaming & one-shot) using
the monitoring gRPC service. Update DQN tests to match post-fix defaults:
IQN disabled, CQL alpha=0.1, v_min/v_max widened, 26D search space.
- train.rs: `fxt train monitor [--once] [--model X] [--interval N]`
- Rewrite gradient collapse test for BF16 mixed precision awareness
- Update inference test config to match trainer defaults (IQN off, CQL on)
- Update production smoke test for 26D parameter space
- Add dqn_action_collapse_fix_test.rs verifying all 6 root cause fixes
- Add planning docs for monitoring service and epoch financial metrics
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add Training cockpit sub-tabs (Live/History) with expandable per-epoch
detail panel showing financial metrics, RL diagnostics, and gradient health.
History tab fetches completed/failed runs from ListTrainingJobs RPC.
Add scrollable pod table to Pods cockpit and compact pods summary to
Overview bottom row. Fix K8s API egress in api-gateway NetworkPolicy
(monitoring-service→prometheus, apply existing K8s API CIDR rules).
Add 62 new unit tests across event_loop (51) and data_fetcher (11)
covering all StateUpdate variants, ring buffer eviction, epoch history
accumulation, pod scroll helpers, and navigation edge cases.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
All 32 MCP tool handlers now make real gRPC calls to the API Gateway
instead of returning stub responses. Covers: service monitoring,
training management, trading operations, ML inference, risk metrics,
broker status, data acquisition, cluster resources, agent control,
config management, and hyperopt tuning.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
end_to_end_tests.rs and error_handling_tests.rs imported non-existent
modules (fxt::client, fxt::prelude, etc.) from a prior architecture.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- StreamSystemStatus now queries real Prometheus data (was all zeros)
- StreamAlerts returns honest unimplemented (was sending empty events)
- Training epoch shows "N" not "N/0" (max_epochs not in proto)
- GPU power shows "300W" not "300W / 0W" when limit unavailable
- Services cockpit shows error reason when service is DOWN
- System status polling detects staleness after 3 consecutive failures
- Training cockpit header shows active K8s job count
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
All 30 MCP tool handlers now return structured JSON error with
isError=true instead of fake "stub: would ..." placeholder responses.
Tool registrations preserved so list_tools still works.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
config_service.proto (package foxhunt.config, 4 RPCs) was a subset of
config.proto (package config, 12 RPCs). Migrated api_gateway to implement
ConfigService from config.proto directly. Removed dead config_service()
method from fxt client.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
57 references updated across source, tests, benches, and config.
Proto package name foxhunt.tli kept for wire compatibility.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace three occurrences of String::new() used as "fetch all" filter
values in gRPC request construction with a named FILTER_ALL constant,
making the intent explicit.
The api_gateway monitoring handler was reviewed but its Prometheus URL
appears only once in tests, so no constant was extracted (single-use).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Use em-dash (—) consistently instead of mix of - and — for "no data"
placeholders across all cockpits (data, trading, risk, services, pods)
- Collapse pipeline status section to single "not connected" message
instead of showing 4 misleading rows of dashes
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Adds a new gRPC stream spawner that subscribes to SubscribeClusterPods
and groups pods by service name for the pods cockpit view. Follows the
same backoff/reconnect pattern used by all other stream spawners.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add K8s pod data structures to TUI state layer for the upcoming pods
cockpit view. PodData represents individual pods, PodGroupData groups
them by service with ready/total counts. The StateUpdate::Pods variant
and apply_update handler wire the data path from gRPC streams to state.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Flip 4 positions in the base64 payload instead of 1 to eliminate
any chance of environmental flakiness from OsRng contention.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Auto-refresh: before non-auth commands, if the access token is expired
but a refresh token exists, silently regenerate tokens. Prints
"Token refreshed." to stderr so users know what happened.
- FXT_PASSWORD: login reads password from this env var when set,
skipping the interactive prompt. Safer than a --password flag
(env vars don't appear in ps output or shell history).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
FoxhuntClient now holds an AuthInterceptor<FileTokenStorage> and
all 11 typed service accessors use with_interceptor() instead of
bare Channel::new(). This ensures every outgoing RPC automatically
includes the JWT Bearer token when one is cached on disk.
The bare channel() accessor is preserved for LoginClient which
needs unauthenticated access for the initial login RPC.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Adds accessor methods for all 11 service clients (trading, ml, ml_training,
broker_gateway, trading_agent, monitoring, config, data_acquisition, risk,
health, config_service). All clients share a single gRPC channel.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Port Prometheus scraping and training metrics RPCs directly into
api_gateway, eliminating the monitoring-service backend proxy.
MonitoringServiceProxy replaced with MonitoringServiceHandler that
queries Prometheus directly for training metrics, GPU stats, and
active job counts.
Changes:
- monitoring_proxy.rs -> monitoring_handler.rs with embedded PrometheusClient
- Remove MonitoringBackendConfig and setup_monitoring_proxy from server.rs
- main.rs reads PROMETHEUS_URL + MONITORING_STREAM_INTERVAL instead of
MONITORING_SERVICE_URL
- Remove monitoring-service from backend health check list
- Monitoring is always available (no graceful degradation needed)
- Fix fxt build.rs compile_protos type mismatch
- All 9 ported unit tests + 0 clippy warnings
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
fxt now compiles from the same service protos as all backend services.
Deleted 8 fat-client protos (3,286 lines) from bin/fxt/proto/.
Updated include_proto! macros to use new package names (trading, ml,
config instead of foxhunt.tli, foxhunt.ml, foxhunt.config).
Added risk, data_acquisition, and config_service proto modules.
Fixed duplicate include_proto in agent.rs to use crate::proto.
Existing command implementations will be rewritten to use new proto
types in Task 6.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add print_financial_metrics() to the monitor command, displaying
per-model Sharpe ratio (color-coded green/yellow/red), win rate,
max drawdown, profit factor, total return, trade count, and
action distribution (BUY/SELL/HOLD percentages). Only shown when
sessions report non-zero financial data.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add Sharpe/Win% columns to training list table, financial summary lines
(Sharpe, Sortino, Win Rate, Max DD, PF, Return, Avg, Trades) to the
detail overview, action distribution (BUY/SELL/HOLD %) to current metrics,
and four new sparklines (Sharpe, Win Rate, Max DD, Total Return) to the
Metrics sub-tab.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
