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fix: LLM analysis + new run button + settings page support

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- 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)
This commit is contained in:
BizzleBot
2026-03-20 21:51:05 +00:00
parent c17b3b5167
commit aba30f7718
8 changed files with 911 additions and 117 deletions
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llm_client/__pycache__/analyzer.cpython-313.pyc
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llm_client/analyzer.py
+164 -24
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@@ -1,15 +1,36 @@
#!/usr/bin/env python3
"""
LLM Accumulation Signal Analyzer -- Calls Ollama on Mac Mini to analyze results
LLM Accumulation Signal Analyzer -- Calls LLM to analyze results
and suggest config modifications for the next iteration.
Supports multiple providers: Ollama, LM Studio, OpenAI, Anthropic, OpenRouter.
"""
import json
import os
import re
import requests
OLLAMA_URL = "http://100.100.242.21:11434"
MODEL = "qwen3.5:27b"
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
LLM_SETTINGS_PATH = os.path.join(BASE_DIR, "config", "llm_settings.json")
# Fallback defaults
DEFAULT_OLLAMA_URL = "http://100.100.242.21:11434"
DEFAULT_MODEL = "qwen3.5:27b"
def load_llm_settings():
"""Load LLM settings from config file, with fallback to defaults."""
if os.path.exists(LLM_SETTINGS_PATH):
with open(LLM_SETTINGS_PATH) as f:
return json.load(f)
return {
"provider": "ollama",
"model": DEFAULT_MODEL,
"providers": {
"ollama": {"base_url": DEFAULT_OLLAMA_URL},
},
}
SYSTEM_PROMPT = """You are a quantitative analyst optimizing a BTC ACCUMULATION SIGNAL model. The goal is NOT day-trading -- it is finding statistically optimal times to BUY BTC for long-term holding.
@@ -120,6 +141,119 @@ You MUST respond with ONLY a JSON object (no markdown, no explanation outside th
The "config" field must contain the COMPLETE config so it can be used directly."""
def _call_ollama(settings, messages):
"""Call Ollama API."""
provider_cfg = settings.get("providers", {}).get("ollama", {})
base_url = provider_cfg.get("base_url", DEFAULT_OLLAMA_URL)
model = settings.get("model", DEFAULT_MODEL)
payload = {
"model": model,
"messages": messages,
"stream": False,
"think": False,
"options": {"temperature": 0.7, "num_predict": 4096},
}
print(f" Calling LLM ({model} via Ollama at {base_url})...")
resp = requests.post(f"{base_url}/api/chat", json=payload, timeout=600)
resp.raise_for_status()
return resp.json()["message"]["content"]
def _call_openai_compatible(settings, messages, provider_name):
"""Call OpenAI-compatible API (LM Studio, OpenAI, OpenRouter)."""
provider_cfg = settings.get("providers", {}).get(provider_name, {})
model = settings.get("model", "")
if provider_name == "lmstudio":
base_url = provider_cfg.get("base_url", "http://100.100.242.21:1234")
url = f"{base_url}/v1/chat/completions"
headers = {"Content-Type": "application/json"}
elif provider_name == "openai":
url = "https://api.openai.com/v1/chat/completions"
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {provider_cfg.get('api_key', '')}",
}
elif provider_name == "openrouter":
url = "https://openrouter.ai/api/v1/chat/completions"
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {provider_cfg.get('api_key', '')}",
}
else:
raise ValueError(f"Unknown OpenAI-compatible provider: {provider_name}")
payload = {
"model": model,
"messages": messages,
"temperature": 0.7,
"max_tokens": 4096,
}
print(f" Calling LLM ({model} via {provider_name})...")
resp = requests.post(url, json=payload, headers=headers, timeout=600)
resp.raise_for_status()
return resp.json()["choices"][0]["message"]["content"]
def _call_anthropic(settings, messages):
"""Call Anthropic Messages API."""
provider_cfg = settings.get("providers", {}).get("anthropic", {})
model = settings.get("model", "claude-sonnet-4-20250514")
api_key = provider_cfg.get("api_key", "")
# Anthropic uses system as a top-level param, not in messages
system_msg = ""
api_messages = []
for m in messages:
if m["role"] == "system":
system_msg = m["content"]
else:
api_messages.append(m)
payload = {
"model": model,
"max_tokens": 4096,
"messages": api_messages,
}
if system_msg:
payload["system"] = system_msg
headers = {
"Content-Type": "application/json",
"x-api-key": api_key,
"anthropic-version": "2023-06-01",
}
print(f" Calling LLM ({model} via Anthropic)...")
resp = requests.post(
"https://api.anthropic.com/v1/messages",
json=payload,
headers=headers,
timeout=600,
)
resp.raise_for_status()
data = resp.json()
# Extract text from content blocks
return "".join(
block["text"] for block in data.get("content", []) if block.get("type") == "text"
)
def call_llm(messages):
"""Route LLM call to the configured provider."""
settings = load_llm_settings()
provider = settings.get("provider", "ollama")
if provider == "ollama":
return _call_ollama(settings, messages)
elif provider in ("lmstudio", "openai", "openrouter"):
return _call_openai_compatible(settings, messages, provider)
elif provider == "anthropic":
return _call_anthropic(settings, messages)
else:
raise ValueError(f"Unknown LLM provider: {provider}")
def analyze_and_suggest(current_config, results, iteration_history=None):
"""
Send current results to LLM and get suggested config modifications.
@@ -161,24 +295,12 @@ def analyze_and_suggest(current_config, results, iteration_history=None):
{history_text}
Analyze these results and suggest 1-3 specific modifications to the config. Return ONLY valid JSON."""
payload = {
"model": MODEL,
"messages": [
{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": user_prompt},
],
"stream": False,
"think": False,
"options": {
"temperature": 0.7,
"num_predict": 4096,
},
}
messages = [
{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": user_prompt},
]
print(f" Calling LLM ({MODEL} on Mac Mini)...")
resp = requests.post(f"{OLLAMA_URL}/api/chat", json=payload, timeout=600)
resp.raise_for_status()
content = resp.json()["message"]["content"]
content = call_llm(messages)
# Strip thinking tags if present
content = re.sub(r"<think>.*?</think>", "", content, flags=re.DOTALL).strip()
@@ -196,7 +318,7 @@ Analyze these results and suggest 1-3 specific modifications to the config. 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)