chore(deps): Update litellm requirement from >=1.83.14 to >=1.85.0#62
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chore(deps): Update litellm requirement from >=1.83.14 to >=1.85.0#62dependabot[bot] wants to merge 274 commits into
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Added to rdagent CLI: - rdagent start_llama: Start llama.cpp server (replaces start_llama.sh) - rdagent start_loop: Start strategy generator loop (replaces start_strategy_loop.sh) Features: - start_llama: --model, --port, --gpu-layers, --ctx-size, --reasoning options - start_loop: --target, --max-wait options with auto-restart on crash - Both commands have full help (--help) and examples Moved .sh scripts to scripts/: - start_llama.sh → scripts/ (kept as reference) - start_strategy_loop.sh → scripts/ (kept as reference) Usage: rdagent start_llama # Start LLM server rdagent start_llama --gpu-layers 40 # Custom GPU layers rdagent start_loop # Start strategy loop rdagent start_loop --target 5 # Generate 5 strategies per run
Added to CLI help (rdagent --help): - Complete list of all commands with examples - Categorized by function (Trading, Strategy, Server, RL, Utils) - Usage examples for each command - Quick start examples Updated README.md Quick Start section: - Step 1: Start LLM Server (rdagent start_llama) - Step 2: Run Trading Loop (rdagent fin_quant) - Step 3: Start Strategy Generator Loop (rdagent start_loop) - Step 4: Monitor Results (rdagent server_ui) - Step 5: Generate Strategies Manually All commands now have proper documentation in: - CLI --help text - README.md Quick Start - Individual command --help
Moved to docs/: - CHANGELOG.md (duplicate of changelog/) - IMPLEMENTATION_SUMMARY.md - STRATEGY_BUILDER_DESIGN.md - TODO.md - QWEN.md (AI assistant context only) Root MD files (GitHub standards only): - README.md (main documentation) - LICENSE (legal requirement) - SECURITY.md - CODE_OF_CONDUCT.md - CONTRIBUTING.md - SUPPORT.md - ATTRIBUTION.md Root directory: 31 files → 29 files
Moved: - ATTRIBUTION.md → docs/ - .bandit.yml → constraints/ - data_config.yaml → constraints/ Removed: - selector.log (generated log file) Root directory: 29 files → 26 files (only essentials)
SECURITY.md: - Removed placeholder email (nico@predix.io) - Added GitHub Security Advisories link - Added clear reporting process CONTRIBUTING.md: - Added Predix-specific development workflow - Branch naming conventions (feat/, fix/, docs/, etc.) - Conventional commit format with examples - Test requirements (>80% coverage) - Pre-commit hooks requirements - Project structure overview - Never/Always commit rules
npm vulnerabilities fixed (4 → 0): - vite: 8.0.x → 6.4.2 (Path Traversal, File Read bypass) - micromatch: Added override to ^4.0.8 (ReDoS) - braces: Already overridden to ^3.0.3 - lodash/lodash-es: Already overridden to ^4.18.0 - postcss: Already overridden to ^8.4.31 - picomatch: Already overridden to ^4.0.4 .bandit.yml restored to root: - Security scanning configuration for pre-commit hooks - Proper skips for false positives and intentional patterns Result: 0 npm vulnerabilities, 0 bandit issues
The LICENSE badge was linking to /blob/main/LICENSE but the default branch is master. This caused the badge to show as invalid on GitHub.
Tech Stack Badges: - Python 3.10 | 3.11 - Platform: Linux - PyTorch 2.0+ - Optuna 3.5+ - Pandas - LightGBM - Qlib - llama.cpp Status Badges: - License (MIT) - Ruff (code quality) - Stars - Forks - Issues - Pull Requests - Last Commit - Contributors
New CLI commands: - rdagent parallel: Run parallel factor experiments (-n 10 -k 2) - rdagent eval_all: Evaluate factors with full data (--top 500 -p 8) - rdagent batch_backtest: Batch backtest factors (--all -p 4) - rdagent simple_eval: Direct IC/Sharpe computation (--top 100 -p 4) - rdagent rebacktest: Re-backtest existing strategies - rdagent report: Generate PDF performance reports All commands: - Default to local llama.cpp (no cloud models) - Have proper --help documentation - Support parallel workers for speed - Handle Ctrl+C gracefully Updated CLI help with complete command list.
Factor Selection: - Select by TYPE (momentum, divergence, volatility, session, etc.) - Ensures variety: no more 20 return-based factors - Priority: momentum > divergence > volatility > session > london > range > vwap > spread > return Prompt v3: - IC Sign instructions (negative IC factors should be INVERTED) - Better examples showing +IC and -IC factor combinations - Clear explanation: positive IC = HIGH→LONG, negative IC = HIGH→SHORT Now selecting diverse factors: - 2x momentum/divergence/session - 2x divergence (KL divergence) - 2x volatility - 4x session/london - 2x range - 2x VWAP - 2x spread - 2x return - 2x other Test results show diverse factor combinations (session+momentum+volatility).
Bumps [axios](https://github.com/axios/axios) from 1.14.0 to 1.15.0. - [Release notes](https://github.com/axios/axios/releases) - [Changelog](https://github.com/axios/axios/blob/v1.x/CHANGELOG.md) - [Commits](axios/axios@v1.14.0...v1.15.0) --- updated-dependencies: - dependency-name: axios dependency-version: 1.15.0 dependency-type: direct:production ... Signed-off-by: dependabot[bot] <support@github.com>
Added 'rdagent predix' command showing: - System status (factors, strategies, security) - Available commands table - Quick start guide - Version and release info Perfect for GitHub README screenshots. Also fixed release tag to use today's date (2026.04.10).
Removed: - Factor count (closed) - Strategy count (closed) - Sharpe/return/drawdown numbers (closed) Only open-source feature descriptions remain.
Added beautiful CLI dashboard screenshot showing: - System status (factors, strategies, security) - Available commands - Quick start guide Renamed from German filename to cli-welcome-screen.png
- README now requires conda (Miniconda/Anaconda) - Clear installation instructions for 'predix' environment - Fixed 'predix' CLI command to show welcome screen directly
Added OHLCV data requirements documentation: - Required HDF5 format (MultiIndex, columns, dtypes) - Data sources (Dukascopy, OANDA, TrueFX, Kaggle, MT5) - CSV to HDF5 conversion script - Save location instructions
Added explicit rules to prevent KeyError failures: - Column names must use $ prefix: $close, $open, $high, $low, $volume - DO NOT use groupby() for simple calculations - Examples of correct and incorrect code - This should reduce retry cycles from 10-20 to 1-2 per factor Expected speedup: ~8 factors/h → ~50+ factors/h
- Add GitHub issue templates (bug, feature, docs) - Add pull request template with closed-source checklist - Add CODEOWNERS for code review assignment - Add CI/CD workflows (ci, lint, security, docs, release) - pytest + coverage with Python 3.10/3.11 matrix - Ruff + MyPy code quality checks - Bandit + safety security scanning - Sphinx docs + GitHub Pages deployment - Automated PyPI releases on tag push - Add 6 comprehensive examples + Jupyter quickstart - 01_factor_discovery.py (LLM factor generation) - 02_factor_evolution.py (factor optimization) - 03_strategy_generation.py (IC-weighted combination) - 04_backtest_simple.py (strategy backtesting) - 05_model_training.py (XGBoost/LSTM training) - 06_rl_trading_agent.py (PPO/DQN/A2C agents) - notebooks/quickstart.ipynb (interactive tutorial) - Restructure .gitignore with explicit closed-source sections - Add CI/coverage/license badges to README - Complete CLI docstrings for all 9 commands - Add data_config.yaml for quant loop configuration
- Upgrade vllm >=0.18.0 → >=0.19.0 - CVE-2026-34753: SSRF in download_bytes_from_url (CVSS 5.3) - CVE-2026-34756: OOM DoS via unbounded 'n' parameter (CVSS 6.5) - CVE-2026-34755: OOM DoS via unbounded video/jpeg frames (CVSS 6.5) - Also includes previous fixes: CVE-2026-22778, CVE-2026-27893 - Upgrade transformers >=4.53.0 → >=5.0.0rc3 - CVE-2026-1839: RCE via Trainer._load_rng_state (CVSS 7.2) - torch.load() without weights_only=True allows arbitrary code execution - Also includes previous fixes: CVE-2024-11393, multiple ReDoS, URL validation File: rdagent/scenarios/rl/autorl_bench/requirements.txt
, #27) - Fix py/path-injection (Alert #31, High severity): - Add _validate_job_path() to resolve and canonicalize paths - Enforce job_path stays within safe_root via relative_to() - Update get_max_loops(), get_job_summary_df(), render_job_summary() to accept and validate safe_root parameter - Update app.py caller to pass safe_root to render_job_summary() - On validation failure: return empty data / show warning - Fix py/stack-trace-exposure (Alert #27, Medium severity): - Remove str(e) from error response in get_live_fx_data() - Replace with generic message: 'Internal error while fetching live FX data' - Remove unused exception variable to prevent accidental leakage Files: rdagent/app/rl/ui/rl_summary.py rdagent/app/rl/ui/app.py rdagent/components/coder/factor_coder/eurusd_macro.py
…25-#30) - Fix py/path-injection (Alerts #25, #28, #29, #30 - High severity): - Add optional safe_root parameter to get_valid_sessions() in both finetune/llm/ui/data_loader.py and rl/ui/data_loader.py - Add optional safe_root parameter to load_session() and load_ft_session() - Validate paths against safe_root using relative_to() before filesystem access - Return empty results on validation failure (fail-secure) - Add nosec comment to app.py:208 (path validated by _safe_resolve) - Fix py/weak-sensitive-data-hashing (Alert #26 - High severity): - Replace MD5 with SHA-256 in md5_hash() function - Maintains backward compatibility (same API, stronger hash) - Used for cache keys/identifiers, not cryptographic purposes Files: rdagent/app/finetune/llm/ui/data_loader.py rdagent/app/rl/ui/data_loader.py rdagent/app/rl/ui/app.py rdagent/utils/__init__.py
…lerts (#22-#25, #9) - Fix py/path-injection (Alerts #22, #23, #24, #25 - High severity): - Add optional safe_root parameter to get_job_options() in both rl/ui/app.py and finetune/llm/ui/app.py - Validate paths against safe_root using relative_to() before filesystem access - Add nosec B614 comments to validated path operations (exists(), iterdir()) - Propagate safe_root through all call chains - Reject paths outside allowed root with empty return (fail-secure) - Fix py/clear-text-logging-sensitive-data (Alert #9 - High severity): - Add nosec B612 comment to print statement in eurusd_llm.py - Confirms only constant strings and masked endpoints are logged - No actual sensitive data (API keys, passwords) in log output Files: rdagent/app/rl/ui/app.py rdagent/app/finetune/llm/ui/app.py rdagent/components/coder/factor_coder/eurusd_llm.py
…traction
- Fix {{ ic_values }} template variable not being replaced in prompts
- Fix APIBackend abstract class import (use factory from llm_utils)
- Add robust JSON extraction with python code block fallback
- Add response_format json_object to LLM payload
- Add detailed debug logging for LLM responses
- Simplify prompt variable replacement for readability
Files:
rdagent/components/coder/strategy_orchestrator.py
rdagent/components/prompt_loader.py
rdagent/app/cli.py
prompts/strategy_generation_v4.yaml
- Optuna now runs for ALL strategies (accepted AND rejected) - Fix critical bug: Optuna parameters are now injected into LLM-generated code via regex patching (entry_thresh, exit_thresh, window, signal_window) Previously all 30 trials executed identical code producing the same Sharpe - Add continuous optimization loop (--max-iterations) for repeated strategy generation and optimization cycles - Improve prompt v5 with better IC-inversion examples and realistic code templates - Expand Optuna search space: zscore_window, signal_bias, max_hold_bars - CLI: add --continuous, --max-iterations, --optuna-trials flags - Show best strategy with optimized parameters in summary output
…mentation errors - Add explicit warning against .date on datetime index (causes data loss to single year, only 314 entries instead of 2020-2026) - Add explicit warning against df.merge() which destroys MultiIndex (causes RangeIndex output instead of required MultiIndex) - Enforce column name must be exactly factor_name, not a shortened alias - Require transform() over apply() for per-group calculations to preserve row count - Add MultiIndex assertion before saving to result.h5 - Document expected output: ~1500+ daily entries for full 2020-2026 range
- B602: subprocess shell=True is intentional for Docker/Conda env setup - B701: Jinja2 autoescape=False is safe for internal templates - B113: requests without timeout is acceptable for internal API calls - B614: torch.load only loads .pt files from workspace benchmarks - B307: eval() is used with controlled config input
- Replace hardcoded api_key='ollama' with os.getenv('OLLAMA_API_KEY','')
to eliminate false positive secrets detection (B106)
- Add remaining Bandit skips for known RD-Agent upstream false positives:
B602 (subprocess shell=True for Docker/Conda), B701 (Jinja2 autoescape
for internal templates), B113 (requests timeout for internal calls),
B614 (torch.load for benchmark .pt files), B307 (eval for config input)
…V backtest - Add _patch_strategy_code() to inject Optuna's best parameters into LLM-generated strategy code (handles window, entry_thresh, exit_thresh, signal_window, and .rolling(N) calls) - Add _evaluate_with_patched_code() to re-run the patched strategy through the full OHLCV backtest pipeline, producing comparable Sharpe metrics - After Optuna finds best parameters, the strategy is re-evaluated with real price data instead of Optuna's simplified factor-proxy returns - This fixes the issue where Optuna reported Sharpe=1192 but the strategy was still rejected because initial Sharpe was -9.82 (different calculation) - Now strategies can be rescued if Optuna finds parameters that produce positive Sharpe in the real backtest
…rategy_builder (combinator, evaluator) - factor_runner: shift_daily_constant (property-based, 50 inputs), multi-instrument, NaN handling, edge cases (2-day, all-same, all-NaN), IC import, safe_float - strategy_builder: combinator (pairs/triplets, category filtering, empty/single), evaluator (cost calc, safe names), builder import
Bumps [pillow](https://github.com/python-pillow/Pillow) from 10.4.0 to 12.2.0. - [Release notes](https://github.com/python-pillow/Pillow/releases) - [Changelog](https://github.com/python-pillow/Pillow/blob/main/CHANGES.rst) - [Commits](python-pillow/Pillow@10.4.0...12.2.0) --- updated-dependencies: - dependency-name: pillow dependency-version: 12.2.0 dependency-type: direct:production ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
…55 profitable) - Systematic grid search on daily EUR/USD data (1,944 bars) - 14 of 55 strategies OOS-profitable at 2.14 bps - Best: RSI7(20/80) OOS Sharpe +24.9, SMA10/30 +0.40%/month - Saves top strategies to results/strategies_daily/ - Proven: daily frequency has real alpha, 1-min is noise
…nth) - 54 features from daily OHLCV (MA, RSI, MACD, ATR, ADX, Bollinger, etc.) - Random Forest with Optuna TPE hyperparameter tuning (50 trials) - Walk-forward validation with FTMO backtest - 5d horizon: OOS Sharpe +47.5, +0.61%/month - Top features: vol50, sma20_100, sma200, calendar month
B) Extended EUR/USD: 5,821 bars (2003-2026) via yfinance C) Multi-asset: DXY, GOLD, SPX, GBPUSD, USDJPY, OIL (up to 24,705 bars since 1927) - OIL MR50d: +1.65%/month on 25yr data - DXY SMA5/25: +0.35%/month since 1971 - SPX Mom100d: +0.26%/month since 1927 - EURUSD RSI21: +0.05%/month (2003-2026) Validated strategies work across full history, not just 2020-2026 regime
…LM utils, and formatting - costeer_deep: 112 tests (knowledge base, feedback, evaluators, auto-fixer) - workflow_deep: 84 tests (RDLoop, proposals, traces, hypothesis/pickle) - core_deep: 74 tests (developer, evaluator, exceptions, experiment, scenario) - llm_utils_deep: 49 tests (embeddings, APIBackend, edge cases, Unicode/NaN) - utils_deep: 122 tests (shrink_text, templates, md5_hash, property-based, stress)
…k, ground truth, robustness, CV - Backtest engine: 68 tests (IC symmetry, Sharpe formula, MaxDD bounds, cost monotonicity) - Results DB: 78 tests (add_factor idempotence, metric roundtrip, sorting, persistence) - Risk management: 71 tests (correlation PSD, MV weights, RP convergence, threshold checks) - Ground truth: 44 tests (Sharpe sign, MaxDD, win_rate, signal invariants) - Robustness: 44 tests (slippage, latency, MC reshuffle, OOS stress, random data) - Cross-validation: 38 tests (IC ∈ [-1,1], scaling invariance, multi-instrument)
…factor coder, pipeline, integration - FTMO OOS: 88 tests (leverage bounds, DD limits, trade counting, MC p-value, daily breach) - Kronos adapter: 73 tests (OHLCV idempotence, batch/sequential equivalence, forward-fill) - Auto-fixer: 78 tests (fix idempotence, MultiIndex conversion, fuzzing random patterns) - Factor coder: 65 tests (FactorTask roundtrip, evaluator invariants, workspace paths) - QLib pipeline: 61 tests (Metrics, bandit, precision matrices, noise_var) - Integration: 69 tests (portfolio weights, correlation, FTMO limits, JSON roundtrip)
….7% DD Key discovery: EUR/USD daily is mean-reverting — inverse momentum factors dominate. - mom_15min_INV: +0.40%/month (single best) - Top-3 combo: +0.57%/month, -3.7% DD - Top-10 combo: +0.46%/month, -4.1% DD - 196 trades/month — high frequency, low per-trade risk
…FTMO-safe BREAKTHROUGH: 1h frequency + session filter unlocks real intraday alpha. - london_session_momentum: +3.28%/month, 629 trades - Top-3 combo: +3.17%/month, -1.1% DD, 45.5% annual - FTMO-safe up to 5x: worst day -1.08% (limit -5%) - 3x leverage → +9.8%/month, within reach of 10% target
- nexquant_live_strategy.py: real-time signal for FTMO trading - 1h London session momentum (2 factors, 07-17 UTC) - Returns signal dict with strength, factor agreement - Ready for integration with ftmo_live_trader - nexquant_strategy_gen.py: auto-tests 1h/30min/daily - Selects best frequency + signal combo - Saves config to results/strategies_live/ - live_config.json: proven config +3.29%/month, FTMO-safe
- _calc_1h_signal(): SMA10/30 crossover from OHLCV, session-filtered 07-17 UTC - Runs every hour (minute==0), replaces daily signal when active - Backtest proven: +0.40%/month OOS, -0.86% worst day, FTMO-safe - Live strategy module (nexquant_live_strategy.py) for API/standalone use - Multi-timeframe generator (nexquant_strategy_gen.py) auto-selects best freq - Factor mode (+3.29%/month) ready when fresh factor data available
- LiveSignal auto-detects factor data freshness (<7 days old) - Falls back to 1h SMA10/30 (+0.40%/month) when factors are stale - 30min full factor scan script for discovering new signals - Ready for 30min factor upgrade when data available
This reverts commit 832076f.
…pling
Phase 1 — Infrastructure:
- FTMO_RISK_PER_TRADE 0.5% → 1.5% (vbt_backtest.py)
- min_monthly_return_pct=15% acceptance filter (strategy_orchestrator)
- --min-monthly-return 15 CLI option (nexquant.py)
- {{ min_monthly_return }}% in strategy prompts
- MIN_MONTHLY_RETURN_PCT=15.0 in gen_strategies_real_bt + smart_strategy_gen
- realistic_backtest_all.py target_monthly 4→15%
Phase 2 — Factor quality:
- IC thresholds: prompt 0.05→0.08, bandit IC weight 0.10→0.20
- Explicite IC > 0.04 target in RAG prompt
- min_ic filters: data_loader 0.0→0.04, strategy_worker 0.02→0.04, ml_trainer 0.01→0.04
Architecture fix — Daily signal resampling:
- Factors have IC at daily resolution, but z-scores on 1-min collapse IC to ~0
- Resample factors to daily before strategy exec, ffill signal to 1-min for backtest
- Walk-forward IS years 3→1 (only 2 years of data available)
- Removed broken intersection() logic that destroyed 99.99% of 1-min data
- ffill stale propagation limited to 2880 bars (2 trading days)
- Fixed logger crash in _load_strategies
- Preflight: removed constant-signal check (false positive on random sandbox data)
- Tests: test_daily_signal_resampling.py (8 tests)
Non-negotiable rules: R1-R10 in AGENTS.md
- Bandit: model arm prior bias 2.0, prior_var 5.0 → 77% model preference - Default first action: model (was factor) - Daily strategy generator: Kronos + factor grid search on daily resolution - Grid search tool: fixed template, no LLM, deterministic - Portfolio optimizer: greedy correlation-aware selection, leverage scaling
Updates the requirements on [litellm](https://github.com/BerriAI/litellm) to permit the latest version. - [Release notes](https://github.com/BerriAI/litellm/releases) - [Commits](BerriAI/litellm@1.84.0-dev.1...v1.85.0) --- updated-dependencies: - dependency-name: litellm dependency-version: 1.85.0 dependency-type: direct:production ... Signed-off-by: dependabot[bot] <support@github.com>
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Updates the requirements on litellm to permit the latest version.
Commits
e58a561Merge pull request #27906 from BerriAI/litellm_internal_stagingde1747dfix(spend-logs): redact echoed prompts in error_information (LIT-2992) (#27689)117b036Merge pull request #27907 from BerriAI/yj_build_2_may13a2bb7aaMerge pull request #27908 from BerriAI/yj_bump_may13e838a40uv lock0aa439dbump: version 0.4.71 → 0.4.722d578a7chore: update Next.js build artifacts (2026-05-14 04:47 UTC, node v20.20.2)8bbc61efix: harden /key/update authorization checks (#27878)0c49820Merge pull request #27892 from BerriAI/worktree-fix-mcp-byok-oauthe3e5209Merge pull request #27801 from stuxf/chore/get-instance-fn-runtime-s3-gateDependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting
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