From aba30f771896d985ad920c9b61574f1194d5e05d Mon Sep 17 00:00:00 2001 From: BizzleBot Date: Fri, 20 Mar 2026 21:51:05 +0000 Subject: [PATCH] fix: LLM analysis + new run button + settings page support - Fixed LLM failing silently (401 auth error on every iteration) - Reset provider to Ollama (working) from broken OpenRouter config - Added /api/clear endpoint + 'New Run' button to reset history - LLM failures now logged visibly with error details - LLM suggestions persisted to iteration data (survive restarts) - Settings page support via llm_settings.json (multi-provider) --- __pycache__/orchestrator.cpython-313.pyc | Bin 27424 -> 28066 bytes config/best_config.json | 68 ++ config/current_config.json | 104 ++- config/llm_settings.json | 21 + dashboard/server.py | 630 +++++++++++++++++- .../__pycache__/analyzer.cpython-313.pyc | Bin 13363 -> 18025 bytes llm_client/analyzer.py | 188 +++++- orchestrator.py | 17 +- 8 files changed, 911 insertions(+), 117 deletions(-) create mode 100644 config/best_config.json create mode 100644 config/llm_settings.json diff --git a/__pycache__/orchestrator.cpython-313.pyc b/__pycache__/orchestrator.cpython-313.pyc index b604ee0faa8a6bf461e94b3afcad861500986995..b0bb0178c2b8ddd5415bd87f0af27fd8a99c8f58 100644 GIT binary patch delta 1945 zcmX|>T~HHO6vy}OX7dGv00{vEvLQr3NdOU$C`O=&R)vvm3?)PeAwVi5xI}GzX!>Ah z`d!=WR37@k*lEX^`cNnBw0&^KQD=>oImGTYCNmBZ$XcP|5|1kG=nGW%h7x=>A6`cTEhSBz9W82 zjKWiVZ%-*gSkv@8eM^Dg>Mahw-?UdGVRncq%A1J4we1L&@Sn14)>u`1X#pwW#YB~X zvSxWjjm#byzS&xZ_F_w$juADvNUKMevHis$`UEGpm7q)Uj0d~gsu~{FFj}ey=_vbt z@`Csyp&F4hjSQyXX`b0t>dlU~W!@ZqgWbL2vMMUjvTo_Yw^P&wXIGZkw|z%8sTHWV(_JQBSMexu%ieq;tP%s0{?)A zUhbYHI+oPmKhPn#okKpa$J-lAfY3QO*za+73qKIUQQFKMP2f0NZA;V6rbZG-8d_SK zTg=>{T++?mdF=#uXo!?@g45gW8{h`rg8z`v<@9@e-tEGd#3OtKfP*Upt^`WCf(RfAI188r#0X+y&rrvZ+b`UNi~~>s*Z`QqEgi*yX^{N@C*U2x z@?`^c3ot_vllcXw%iZO49f*niuaJFFLf6`O*P!6>`a9e&yM)`2JO=oQpooh}UC#dg zcvZ5?=k4+IcK8mv1;Nwp7Vdy8@H3EHUrK=MBXk3P0lW#g3%Cb}FD!l#eueNi!0!MF z;64Bjs&EDH2jBtVPrwm^7&;(4gy1g#oR;{yG6V(L|Cof#sbgTMSC}ImLmUBicIM!v zdKo^}sYy^1kDdI6^2PYGPLAG52fyolk3m{&>wepqNSUgR%Z5uvmFuGGONcU#Dhs2T zdMuvv&{URXVK+m`(ua42rG<+}@RRO$1F4iMXMsviDw!e1Y~z{kFUmgejhMEMnzn|F zmT!dfy7OtFtcK%~DW!TiVWe@SI+Si4Q<|d6%!pDys??7u4Y$;~Db<=#wsBNtil{0^ zRTWW#DOzAUedM$!RAmhr_RXb?Kz} z-e4xYqSaU{*(;Ucx|$&0C#A2dEks{aX>5h;we_`Ny^b_CGkZOwehUe1sKI+F$^>8jzY^A%u#LAb{lj9SqBUu4lwh2(~hpZT9B84-V&EA zMq=WUnW>AOFPh9`S>iXpkZO!ZKh4C*CMu;AiVpYU)cHaY!m_Za&(j8Na)0N4&VA0g zH}~AzE6eoq0yV80jWU5>?80Zm?Jd)$djWbj{vfcz(;IB5`2byu8;@o5^dbAZ{g&#%_kK1zs7v^@(}8rnYpf9oO2VRpH$yu_~faXr5?sXmd9^)9&hrEvDULs6C zAUBW|RO(x9^tMP2OwWJTZ0bq`eTh)I!moVuQGkM3!I)WDhnCKd5?_wunDFm^|ES}l7)KAHd%E_$*OF2 z$!fhJS)0vAY{61-VV6H>5mN;cRHiH^i|J|ss?sis*^Miuy>hsSPuH2lCVtr@hKtzx@XJ(U+Q=i~ zPI#Cr?7fi_G|5&+p3-Gs_BnQJbPru(L!;fkHQ3v<*n%I8CPA^bg#$wS#K36oU4_3j zlKYNu*R`E&6Xx3EC1ZoMvXP^vzuE+jS~4;b^3R`5@Fh!v{@fE8o(MM04paXH1D0mK diff --git a/config/best_config.json b/config/best_config.json new file mode 100644 index 0000000..b6bbe12 --- /dev/null +++ b/config/best_config.json @@ -0,0 +1,68 @@ +{ + "model_type": "xgboost", + "features": { + "use_price_position": true, + "use_momentum": true, + "use_volatility": true, + "use_volume": true, + "use_cycle": true, + "use_pca": false, + "pca_variance": 0.95, + "use_scaler": true + }, + "target": { + "type": "regression", + "forward_periods_1h": [ + 168, + 720, + 2160 + ], + "forward_periods_4h": [ + 42, + 180, + 540 + ], + "weights": [ + 0.2, + 0.3, + 0.5 + ], + "score_range": [ + 0, + 100 + ] + }, + "hyperparameters": { + "learning_rate": 0.01, + "max_depth": 4, + "n_estimators": 300, + "subsample": 0.8, + "colsample_bytree": 0.8, + "min_child_weight": 20, + "gamma": 0.3, + "reg_alpha": 0.5, + "reg_lambda": 3.0, + "lstm_hidden_size": 128, + "lstm_num_layers": 2, + "lstm_dropout": 0.3, + "lstm_epochs": 100, + "lstm_batch_size": 64, + "lstm_sequence_length": 30, + "lstm_patience": 10 + }, + "strategy": { + "strong_buy_threshold": 65, + "good_buy_threshold": 55, + "poor_threshold": 35 + }, + "training": { + "rolling_window": true, + "rolling_train_size": 2500, + "rolling_test_size": 300, + "walk_forward_windows": 5, + "train_pct": 0.7, + "validation_pct": 0.15, + "test_pct": 0.15 + }, + "timeframe": "4h" +} \ No newline at end of file diff --git a/config/current_config.json b/config/current_config.json index 9ac6e87..b6bbe12 100644 --- a/config/current_config.json +++ b/config/current_config.json @@ -1,92 +1,68 @@ { - "model_type": "hybrid", + "model_type": "xgboost", "features": { - "technical_indicators": [ - "RSI_14", - "RSI_7", - "MACD_line", - "MACD_signal", - "MACD_hist", - "BB_upper", - "BB_lower", - "BB_width", - "ATR_14", - "SMA_20", - "SMA_50", - "EMA_10", - "EMA_20", - "OBV", - "stoch_k", - "stoch_d", - "williams_r", - "CCI_20", - "ROC_10" - ], - "lookback_periods": [ - 3, - 5, - 10, - 20 - ], - "use_volume_features": true, - "use_volatility_features": true, - "use_candle_patterns": false, - "use_lag_features": true, - "lag_periods": [ - 1, - 2, - 3, - 5 - ], - "use_pca": true, + "use_price_position": true, + "use_momentum": true, + "use_volatility": true, + "use_volume": true, + "use_cycle": true, + "use_pca": false, "pca_variance": 0.95, "use_scaler": true }, "target": { - "type": "classification", - "direction": "both", - "horizon_candles": 4, - "threshold_pct": 1.0 + "type": "regression", + "forward_periods_1h": [ + 168, + 720, + 2160 + ], + "forward_periods_4h": [ + 42, + 180, + 540 + ], + "weights": [ + 0.2, + 0.3, + 0.5 + ], + "score_range": [ + 0, + 100 + ] }, "hyperparameters": { - "learning_rate": 0.001, - "max_depth": 5, + "learning_rate": 0.01, + "max_depth": 4, "n_estimators": 300, "subsample": 0.8, "colsample_bytree": 0.8, - "min_child_weight": 5, + "min_child_weight": 20, "gamma": 0.3, - "reg_alpha": 0.1, - "reg_lambda": 5.0, + "reg_alpha": 0.5, + "reg_lambda": 3.0, "lstm_hidden_size": 128, "lstm_num_layers": 2, "lstm_dropout": 0.3, "lstm_epochs": 100, "lstm_batch_size": 64, - "lstm_sequence_length": 20, + "lstm_sequence_length": 30, "lstm_patience": 10 }, "strategy": { - "entry_threshold": 0.65, - "exit_type": "trailing_stop", - "stop_loss_pct": 2.0, - "take_profit_pct": 4.0, - "trailing_stop_pct": 1.5, - "position_sizing": "confidence_scaled", - "max_position_pct": 100, - "min_confidence_to_trade": 0.5, - "dynamic_sl_tp": true, - "atr_sl_multiplier": 1.2, - "atr_tp_multiplier": 3.0 + "strong_buy_threshold": 65, + "good_buy_threshold": 55, + "poor_threshold": 35 }, "training": { + "rolling_window": true, + "rolling_train_size": 2500, + "rolling_test_size": 300, "walk_forward_windows": 5, "train_pct": 0.7, "validation_pct": 0.15, - "test_pct": 0.15, - "rolling_window": true, - "rolling_train_size": 3000, - "rolling_test_size": 200 + "test_pct": 0.15 }, "timeframe": "4h" } \ No newline at end of file diff --git a/config/llm_settings.json b/config/llm_settings.json new file mode 100644 index 0000000..2e532c5 --- /dev/null +++ b/config/llm_settings.json @@ -0,0 +1,21 @@ +{ + "provider": "ollama", + "model": "qwen3.5:27b", + "providers": { + "ollama": { + "base_url": "http://100.100.242.21:11434" + }, + "lmstudio": { + "base_url": "http://100.100.242.21:1234" + }, + "openai": { + "api_key": "" + }, + "anthropic": { + "api_key": "" + }, + "openrouter": { + "api_key": "" + } + } +} diff --git a/dashboard/server.py b/dashboard/server.py index 72aa0e8..1df07ab 100644 --- a/dashboard/server.py +++ b/dashboard/server.py @@ -9,6 +9,7 @@ import os import sys import threading +import requests from fastapi import FastAPI from fastapi.responses import FileResponse, HTMLResponse, JSONResponse from pydantic import BaseModel @@ -23,6 +24,7 @@ app = FastAPI(title="BTC Accumulation Signal Optimizer") CONFIG_DIR = os.path.join(BASE_DIR, "config") RESULTS_DIR = os.path.join(BASE_DIR, "results") ITERATIONS_LOG = os.path.join(RESULTS_DIR, "iterations.jsonl") +LLM_SETTINGS_PATH = os.path.join(CONFIG_DIR, "llm_settings.json") _opt_thread = None @@ -31,6 +33,64 @@ class ConfigUpdate(BaseModel): config: dict +class LLMSettingsUpdate(BaseModel): + provider: str + model: str + providers: dict + + +class TestConnectionRequest(BaseModel): + provider: str + providers: dict + + +class FetchModelsRequest(BaseModel): + provider: str + providers: dict + + +def _load_llm_settings(): + if os.path.exists(LLM_SETTINGS_PATH): + with open(LLM_SETTINGS_PATH) as f: + return json.load(f) + return { + "provider": "ollama", + "model": "qwen3.5:27b", + "providers": { + "ollama": {"base_url": "http://100.100.242.21:11434"}, + "lmstudio": {"base_url": "http://100.100.242.21:1234"}, + "openai": {"api_key": ""}, + "anthropic": {"api_key": ""}, + "openrouter": {"api_key": ""}, + }, + } + + +def _mask_api_key(key): + if not key or len(key) < 8: + return "" + return "••••••••" + key[-4:] + + +def _safe_settings(settings): + """Return settings with API keys masked.""" + out = json.loads(json.dumps(settings)) + for name, cfg in out.get("providers", {}).items(): + if "api_key" in cfg: + cfg["api_key"] = _mask_api_key(cfg["api_key"]) + return out + + +def _merge_api_keys(new_providers, existing_providers): + """Preserve existing API keys when the incoming value is masked.""" + for name, cfg in new_providers.items(): + if "api_key" in cfg: + masked = cfg["api_key"] + if masked.startswith("••••") or masked == "": + existing_key = existing_providers.get(name, {}).get("api_key", "") + cfg["api_key"] = existing_key + + @app.get("/api/status") def api_status(): return orchestrator.get_status() @@ -91,7 +151,11 @@ def api_best(): with open(best_path) as f: config = json.load(f) iterations = orchestrator.load_iteration_history() - best_iter = max(iterations, key=lambda x: x.get("cost_improvement", 0)) if iterations else {} + best_iter = ( + max(iterations, key=lambda x: x.get("cost_improvement", 0)) + if iterations + else {} + ) return {"config": config, "best_iteration": best_iter} @@ -110,32 +174,155 @@ def api_download_best_config(): return JSONResponse({"error": "No best config yet"}, status_code=404) -DASHBOARD_HTML = """ - - - - -BTC Accumulation Signal Optimizer - - - - @@ -190,14 +411,16 @@ textarea.config-editor{width:100%;height:300px;background:var(--bg);color:var(--

₿ Accumulation Signal Optimizer

-
+
Idle + """ + NAV_HTML + """
+
Best Cost Improvement
@@ -262,6 +485,8 @@ textarea.config-editor{width:100%;height:300px;background:var(--bg);color:var(--
+ +""" + + @app.get("/", response_class=HTMLResponse) def dashboard(): return DASHBOARD_HTML +@app.get("/settings", response_class=HTMLResponse) +def settings_page(): + return SETTINGS_HTML + + if __name__ == "__main__": import uvicorn uvicorn.run(app, host="0.0.0.0", port=3088) + + +@app.post("/api/clear") +def api_clear(): + """Clear iteration history for a fresh run.""" + import glob + results_dir = os.path.join(BASE_DIR, "results") + + # Clear iterations log + log_path = os.path.join(results_dir, "iterations.jsonl") + if os.path.exists(log_path): + os.remove(log_path) + + # Clear individual result files + for f in glob.glob(os.path.join(results_dir, "results_iter_*.json")): + os.remove(f) + + # Reset best config to initial + initial = os.path.join(BASE_DIR, "config", "initial_config.json") + best = os.path.join(BASE_DIR, "config", "best_config.json") + current = os.path.join(BASE_DIR, "config", "current_config.json") + if os.path.exists(initial): + import shutil + shutil.copy(initial, best) + shutil.copy(initial, current) + + # Clear in-memory status + orchestrator._status["llm_suggestions"] = [] + orchestrator._status["best_score"] = 0.0 + orchestrator._status["iteration"] = 0 + + return {"ok": True, "message": "History cleared. Ready for fresh run."} diff --git a/llm_client/__pycache__/analyzer.cpython-313.pyc b/llm_client/__pycache__/analyzer.cpython-313.pyc index 8bf7f7af6ea8e5bf81bcbbcccf0be46ae14b6ea7..28a945a92505503347f89a42c16f7b0d3ef0d056 100644 GIT binary patch literal 18025 zcmb_@3v3(do!^Z35FdI!DC=Q)^s+@+qTYVUl4ZRtKO{@mh`ZjCw+uy66xV!cXGq)P zZgNV1L+qsJ+IyF?HaOfW%>nVE1!CZULIJLKPJ2KW#kHVH@0pn_4qG(n)hG(c>+JONMT6ocB*Sb|i>v)P%}&yjUjH z-mF7wwR+70PO87jFPwS3ZBW(gRMJtgfc_iAe$pjyV!2o$R*F?(wdfFQ#3N#@Sl7*o z_1Af^K|CrpipNAjY;u{zX0b(V728|}@wls4YP?y6c8}g{)LTlRMd_F%+-z#*#1l7# z60W~M?^WQ~({_w+QtdatL_5bsr`U0}U~ipZL9n2S>*U%sVn23vr9j}0VblV5?d=i6 zF>Zx-b|(LwZDwXx81wlOkwn-V55=N_7z#$cVPOo#dNLphU0uS2HyoA)qKwA`ulhoe z0&*f8mu=ptUyu{QU_g!wzF2f66ci#ce`qD-W9`YpN=y>ss{tVzxEmKj@qomhyKG`& zZ7n9nWdU8sLu=uHuqMTBhx`Fa9unrlVQ<9SDWFd=p74iaox0efeu_#t&lWSO0+Gd)!j?-Gasl*$22;ARgOyEl~^X z(l=G#7H>DlEm`%quhSK9QEn*<2@6F!W$te?=jQZQWdaoZr?Zv7wQ^#?Qi)Ct<~1yp z>A!mT9$qxM46WR6VH81Q6jSp2Ow8+NETFHcup-4G`i`%J!hufVPAI-AtbkdTy}nz- 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Retu elif content[i] == "}": depth -= 1 if depth == 0: - parsed = json.loads(content[brace_start:i + 1]) + parsed = json.loads(content[brace_start : i + 1]) break else: raise ValueError("Could not find complete JSON in LLM response") @@ -207,7 +329,14 @@ Analyze these results and suggest 1-3 specific modifications to the config. Retu changes = parsed.get("changes", []) new_config = parsed.get("config", current_config) - required_keys = ["model_type", "features", "target", "hyperparameters", "strategy", "training"] + required_keys = [ + "model_type", + "features", + "target", + "hyperparameters", + "strategy", + "training", + ] for key in required_keys: if key not in new_config: new_config[key] = current_config[key] @@ -218,6 +347,7 @@ Analyze these results and suggest 1-3 specific modifications to the config. Retu if __name__ == "__main__": import sys + config_path = sys.argv[1] if len(sys.argv) > 1 else "config/initial_config.json" with open(config_path) as f: config = json.load(f) @@ -234,8 +364,18 @@ if __name__ == "__main__": "avg_score_at_actual_bottoms": 68.5, "avg_score_at_actual_tops": 35.2, "per_window_cost_improvement": [7.1, 9.3, 8.8, 10.2, 7.0], - "score_distribution": {"0-20": 80, "20-40": 150, "40-60": 200, "60-80": 130, "80-100": 40}, - "feature_importances": {"dist_from_ath_pct": 0.18, "RSI_14": 0.12, "price_percentile_365": 0.10}, + "score_distribution": { + "0-20": 80, + "20-40": 150, + "40-60": 200, + "60-80": 130, + "80-100": 40, + }, + "feature_importances": { + "dist_from_ath_pct": 0.18, + "RSI_14": 0.12, + "price_percentile_365": 0.10, + }, } new_config, reasoning = analyze_and_suggest(config, dummy_results) diff --git a/orchestrator.py b/orchestrator.py index daec0f2..4234a64 100755 --- a/orchestrator.py +++ b/orchestrator.py @@ -461,9 +461,22 @@ def run_optimization_loop(callback=None, config_override=None): "iteration": iteration, "reasoning": reasoning, }) + # Also persist LLM suggestion to iteration log + iter_data["llm_reasoning"] = reasoning + iter_data["llm_applied"] = True config = new_config - except Exception: - import random + except Exception as e: + import random, traceback + err_msg = f"LLM call failed: {type(e).__name__}: {e}" + print(f" WARNING: {err_msg}") + traceback.print_exc() + with _status_lock: + _status["llm_suggestions"].append({ + "iteration": iteration, + "reasoning": f"ERROR: {err_msg} — using random perturbation", + }) + iter_data["llm_reasoning"] = err_msg + iter_data["llm_applied"] = False hp = config.get("hyperparameters", {}) hp["learning_rate"] = hp.get("learning_rate", 0.01) * random.uniform(0.8, 1.2) hp["max_depth"] = max(3, min(10, hp.get("max_depth", 5) + random.choice([-1, 0, 1])))