master_dataset.jsonl

{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 0:", "output": "\"\"\"\nOrder Block Scalper - Comprehensive \nAI Integration & ML Training Script\nVersion: 2. 0\nAuthor: Kenny\nThis script provides:\n1. AI API integration for 20+ cloud \nproviders\n2. Local LLM support (Ollama, LM \nStudio, llama. cpp, etc. )\n3. HuggingFace model integration\n4. Google Cloud Vertex AI support\n5. ML model training from exported EA \ndata\n6. Model inference server for real-\ntime predictions\n\"\"\"\nimport os\nimport json\nimport pandas as pd\nimport numpy as np\nfrom datetime import datetime\nimport requests\nfrom typing import Dict, List, Tuple, \nOptional\nimport joblib\nfrom sklearn. ensemble import \nRandomForestClassifier, \nGradientBoostingClassifier\nfrom sklearn. model_selection import \ntrain_test_split, cross_val_score\nfrom sklearn. preprocessing import \nStandardScaler\nfrom sklearn. metrics import \nclassification_report, \nconfusion_matrix\nimport warnings\nwarnings."}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 1:", "output": "preprocessing import \nStandardScaler\nfrom sklearn. metrics import \nclassification_report, \nconfusion_matrix\nimport warnings\nwarnings. filterwarnings('ignore')\n#====================================\n=====================================\n====\n# CONFIGURATION - ALL AI PROVIDERS\n#====================================\n=====================================\n====\nclass AIProviderConfig:\n\u00a0\u00a0\u00a0 \"\"\"Configuration for all AI \nproviders\"\"\"\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 # OpenAI\n\u00a0\u00a0\u00a0 OPENAI_API_KEY = \"your-openai-\napi-key\"\n\u00a0\u00a0\u00a0 OPENAI_URL = \n\"https://api. openai. com/v1/chat/compl\netions\"\n\u00a0\u00a0\u00a0 OPENAI_MODEL = \"gpt-4-turbo\"\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 # Anthropic (Claude)\n\u00a0\u00a0\u00a0 ANTHROPIC_API_KEY = \"your-\nanthropic-api-key\"\n\u00a0\u00a0\u00a0 ANTHROPIC_URL = \n\"https://api. anthropic. com/v1/message\ns\"\n\u00a0\u00a0\u00a0 ANTHROPIC_MODEL = \"claude-3-5-\nsonnet-20241022\"\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 # Google Gemini\n\u00a0\u00a0\u00a0 GOOGLE_API_KEY = \"your-google-\napi-key\"\n\u00a0\u00a0\u00a0 GOOGLE_URL = \n\"https://generativelanguage."}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 2:", "output": "googleapi\ns. com/v1/models/gemini-\npro:generateContent\"\n\u00a0\u00a0\u00a0 GOOGLE_MODEL = \"gemini-pro\"\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 # DeepSeek\n\u00a0\u00a0\u00a0 DEEPSEEK_API_KEY = \"your-\ndeepseek-api-key\"\n\u00a0\u00a0\u00a0 DEEPSEEK_URL = \n\"https://api. deepseek. com/v1/chat/com\npletions\"\n\u00a0\u00a0\u00a0 DEEPSEEK_MODEL = \"deepseek-chat\"\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 # Perplexity\n\u00a0\u00a0\u00a0 PERPLEXITY_API_KEY = \"your-\nperplexity-api-key\"\n\u00a0\u00a0\u00a0 PERPLEXITY_URL = \n\"https://api. perplexity. ai/chat/compl\netions\"\n\u00a0\u00a0\u00a0 PERPLEXITY_MODEL = \"sonar-pro\"\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 # Cohere\n\u00a0\u00a0\u00a0 COHERE_API_KEY = \"your-cohere-\napi-key\"\n\u00a0\u00a0\u00a0 COHERE_URL = \n\"https://api. cohere. ai/v1/chat\"\n\u00a0\u00a0\u00a0 COHERE_MODEL = \"command-r-plus\"\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 # Mistral\n\u00a0\u00a0\u00a0 MISTRAL_API_KEY = \"your-mistral-\napi-key\"\n\u00a0\u00a0\u00a0 MISTRAL_URL = \n\"https://api. mistral."}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 3:", "output": "mistral. ai/v1/chat/compl\netions\"\n\u00a0\u00a0\u00a0 MISTRAL_MODEL = \"mistral-large-\nlatest\"\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 # xAI (Grok)\n\u00a0\u00a0\u00a0 XAI_API_KEY = \"your-xai-api-key\"\n\u00a0\u00a0\u00a0 XAI_URL = \n\"https://api. x. ai/v1/chat/completions\n\"\n\u00a0\u00a0\u00a0 XAI_MODEL = \"grok-2\"\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 # Groq\n\u00a0\u00a0\u00a0 GROQ_API_KEY = \"your-groq-api-\nkey\"\n\u00a0\u00a0\u00a0 GROQ_URL = \n\"https://api. groq. com/openai/v1/chat/\ncompletions\"\n\u00a0\u00a0\u00a0 GROQ_MODEL = \"llama-3. 3-70b-\nversatile\"\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 # Together AI\n\u00a0\u00a0\u00a0 TOGETHER_API_KEY = \"your-\ntogether-api-key\"\n\u00a0\u00a0\u00a0 TOGETHER_URL = \n\"https://api. together. xyz/v1/chat/com\npletions\"\n\u00a0\u00a0\u00a0 TOGETHER_MODEL = \"meta-\nllama/Meta-Llama-3."}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 4:", "output": "xyz/v1/chat/com\npletions\"\n\u00a0\u00a0\u00a0 TOGETHER_MODEL = \"meta-\nllama/Meta-Llama-3. 1-70B-Instruct-\nTurbo\"\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 # Replicate\n\u00a0\u00a0\u00a0 REPLICATE_API_KEY = \"your-\nreplicate-api-key\"\n\u00a0\u00a0\u00a0 REPLICATE_URL = \n\"https://api. replicate. com/v1/predict\nions\"\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 # HuggingFace\n\u00a0\u00a0\u00a0 HUGGINGFACE_API_KEY = \"your-\nhuggingface-api-key\"\n\u00a0\u00a0\u00a0 HUGGINGFACE_REPO = \"meta-\nllama/Meta-Llama-3-70B-Instruct\"\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 # Azure OpenAI\n\u00a0\u00a0\u00a0 AZURE_API_KEY = \"your-azure-api-\nkey\"\n\u00a0\u00a0\u00a0 AZURE_ENDPOINT = \"https://your-\nresource. openai. azure. com\"\n\u00a0\u00a0\u00a0 AZURE_DEPLOYMENT = \"gpt-4\"\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 # AWS Bedrock\n\u00a0\u00a0\u00a0 AWS_ACCESS_KEY = \"your-aws-\naccess-key\"\n\u00a0\u00a0\u00a0 AWS_SECRET_KEY = \"your-aws-\nsecret-key\"\n\u00a0\u00a0\u00a0 AWS_REGION = \"us-east-1\"\n\u00a0\u00a0\u00a0 AWS_MODEL = \"anthropic."}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 5:", "output": "claude-3-\nsonnet-20240229-v1:0\"\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 # Google Cloud Vertex AI\n\u00a0\u00a0\u00a0 GCP_PROJECT_ID = \"your-gcp-\nproject-id\"\n\u00a0\u00a0\u00a0 GCP_LOCATION = \"us-central1\"\n\u00a0\u00a0\u00a0 GCP_MODEL = \"gemini-pro\"\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 # Fireworks AI\n\u00a0\u00a0\u00a0 FIREWORKS_API_KEY = \"your-\nfireworks-api-key\"\n\u00a0\u00a0\u00a0 FIREWORKS_URL = \n\"https://api. fireworks. ai/inference/v\n1/chat/completions\"\n\u00a0\u00a0\u00a0 FIREWORKS_MODEL = \n\"accounts/fireworks/models/llama-\nv3p1-70b-instruct\"\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 # Anyscale\n\u00a0\u00a0\u00a0 ANYSCALE_API_KEY = \"your-\nanyscale-api-key\"\n\u00a0\u00a0\u00a0 ANYSCALE_URL = \n\"https://api. endpoints. anyscale."}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 6:", "output": "endpoints. anyscale. com/v\n1/chat/completions\"\n\u00a0\u00a0\u00a0 ANYSCALE_MODEL = \"meta-\nllama/Meta-Llama-3-70B-Instruct\"\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 # Cloudflare AI\n\u00a0\u00a0\u00a0 CLOUDFLARE_ACCOUNT_ID = \"your-\ncloudflare-account-id\"\n\u00a0\u00a0\u00a0 CLOUDFLARE_API_KEY = \"your-\ncloudflare-api-key\"\n\u00a0\u00a0\u00a0 CLOUDFLARE_MODEL = \n\"@cf/meta/llama-3-8b-instruct\"\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 # AI21\n\u00a0\u00a0\u00a0 AI21_API_KEY = \"your-ai21-api-\nkey\"\n\u00a0\u00a0\u00a0 AI21_URL = \n\"https://api. ai21. com/studio/v1/chat/\ncompletions\"\n\u00a0\u00a0\u00a0 AI21_MODEL = \"jamba-1. 5-large\"\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 # Local Models\n\u00a0\u00a0\u00a0 OLLAMA_URL = \n\"http://localhost:11434/api/chat\"\n\u00a0\u00a0\u00a0 OLLAMA_MODEL = \"llama3. 1:70b\"\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 LM_STUDIO_URL = \n\"http://localhost:1234/v1/chat/comple\ntions\"\n\u00a0\u00a0\u00a0 LM_STUDIO_MODEL = \"local-model\"\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 GPT4ALL_URL ="}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 7:", "output": "LM_STUDIO_MODEL = \"local-model\"\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 GPT4ALL_URL = \n\"http://localhost:4891/v1/chat/comple\ntions\"\n\u00a0\u00a0\u00a0 GPT4ALL_MODEL = \"gpt4all-13b\"\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 LOCALAI_URL = \n\"http://localhost:8080/v1/chat/comple\ntions\"\n\u00a0\u00a0\u00a0 LOCALAI_MODEL = \"gguf-model\"\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 KOBOLDCPP_URL = \n\"http://localhost:5001/api/v1/generat\ne\"\n\u00a0\u00a0\u00a0 KOBOLDCPP_MODEL = \"kobold-model\"\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 TEXTGEN_WEBUI_URL = \n\"http://localhost:5000/v1/chat/comple\ntions\"\n\u00a0\u00a0\u00a0 TEXTGEN_WEBUI_MODEL = \"current-\nmodel\"\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 VLLM_URL = \n\"http://localhost:8000/v1/chat/comple\ntions\"\n\u00a0\u00a0\u00a0 VLLM_MODEL = \"meta-llama/Meta-\nLlama-3-70B-Instruct\"\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 LLAMACPP_URL = \n\"http://localhost:8080/completion\"\n\u00a0\u00a0\u00a0 LLAMACPP_MODEL = \"llama-\nmodel."}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 8:", "output": "LLAMACPP_URL = \n\"http://localhost:8080/completion\"\n\u00a0\u00a0\u00a0 LLAMACPP_MODEL = \"llama-\nmodel. gguf\"\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 # ML Settings\n\u00a0\u00a0\u00a0 ML_DATA_PATH = \n\"C:/Users/YourName/AppData/Roaming/Me\ntaQuotes/Terminal/Common/Files/ML_Dat\na/\"\n\u00a0\u00a0\u00a0 MODEL_OUTPUT_PATH = \n\". /trained_models/\"\n\u00a0\u00a0\u00a0 FEATURE_COUNT = 25\n\u00a0\u00a0\u00a0 RANDOM_STATE = 42\n\u00a0\u00a0\u00a0 TEST_SIZE = 0. 2\n\u00a0\u00a0\u00a0 CV_FOLDS = 5\n#====================================\n=====================================\n====\n# UNIVERSAL AI CLIENT - ALL PROVIDERS\n#====================================\n=====================================\n====\nclass UniversalAIClient:\n\u00a0\u00a0\u00a0 \"\"\"Universal client supporting \nall AI providers\"\"\"\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 def __init__(self, provider: str \n= \"OpenAI\", api_key: str = None, \n**kwargs):\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 self. provider = provider\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 self. api_key = api_key or \nself. _get_default_key(provider)\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 self."}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 9:", "output": "provider = provider\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 self. api_key = api_key or \nself. _get_default_key(provider)\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 self. config = kwargs\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 def _get_default_key(self, \nprovider: str) -> str:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"\"\"Get default API key for \nprovider\"\"\"\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 key_map = {\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"OpenAI\": \nAIProviderConfig. OPENAI_API_KEY,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"Anthropic\": \nAIProviderConfig. ANTHROPIC_API_KEY,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"Google\": \nAIProviderConfig. GOOGLE_API_KEY,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"DeepSeek\": \nAIProviderConfig. DEEPSEEK_API_KEY,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"Perplexity\": \nAIProviderConfig. PERPLEXITY_API_KEY,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"Cohere\": \nAIProviderConfig. COHERE_API_KEY,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"Mistral\": \nAIProviderConfig. MISTRAL_API_KEY,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"xAI\": \nAIProviderConfig. XAI_API_KEY,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"Groq\": \nAIProviderConfig. GROQ_API_KEY,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"Together\": \nAIProviderConfig. TOGETHER_API_KEY,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"Replicate\": \nAIProviderConfig."}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 10:", "output": "TOGETHER_API_KEY,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"Replicate\": \nAIProviderConfig. REPLICATE_API_KEY,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"Huggingface\": \nAIProviderConfig. HUGGINGFACE_API_KEY,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"Fireworks\": \nAIProviderConfig. FIREWORKS_API_KEY,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"Anyscale\": \nAIProviderConfig. ANYSCALE_API_KEY,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"AI21\": \nAIProviderConfig. AI21_API_KEY,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 }\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return key_map. get(provider, \n\"\")\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 def analyze_market(self, \nmarket_data: Dict) -> Dict:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"\"\"Analyze market using \nselected provider\"\"\"\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 # Route to appropriate \nprovider\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 provider_methods = {\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"OpenAI\": \nself. _call_openai,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"Anthropic\": \nself. _call_anthropic,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"Google\": \nself. _call_google,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"DeepSeek\": \nself. _call_deepseek,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"Perplexity\": \nself. _call_perplexity,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"Cohere\": \nself. _call_cohere,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"Mistral\": \nself."}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 11:", "output": "_call_cohere,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"Mistral\": \nself. _call_mistral,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"xAI\": self. _call_xai,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"Groq\": self. _call_groq,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"Together\": \nself. _call_together,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"Replicate\": \nself. _call_replicate,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"Huggingface\": \nself. _call_huggingface,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"Azure-OpenAI\": \nself. _call_azure,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"AWS-Bedrock\": \nself. _call_bedrock,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"Google-Vertex\": \nself. _call_vertex,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"Fireworks\": \nself. _call_fireworks,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"Anyscale\": \nself. _call_anyscale,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"Cloudflare-AI\": \nself. _call_cloudflare,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"AI21\": self. _call_ai21,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"Ollama\": \nself. _call_ollama,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"LM-Studio\": \nself. _call_lm_studio,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"GPT4All\": \nself. _call_gpt4all,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"LocalAI\": \nself."}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 12:", "output": "_call_gpt4all,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"LocalAI\": \nself. _call_localai,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"KoboldCPP\": \nself. _call_koboldcpp,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"Text-Generation-WebUI\": \nself. _call_textgen_webui,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"vLLM\": self. _call_vllm,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"llama. cpp\": \nself. _call_llamacpp,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 }\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 method = \nprovider_methods. get(self. provider)\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 if not method:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return {\"direction\": 0, \n\"confidence\": 0, \"reason\": \nf\"Unsupported provider: \n{self. provider}\"}\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 try:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return \nmethod(market_data)\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 except Exception as e:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(f\"Error calling \n{self."}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 13:", "output": "provider}: {e}\")\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return {\"direction\": 0, \n\"confidence\": 0, \"reason\": f\"API \nError: {str(e)}\"}\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 def _create_prompt(self, \nmarket_data: Dict) -> str:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"\"\"Create analysis prompt \nfrom market data\"\"\"\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return f\"\"\"\nAnalyze this forex market setup and \nprovide a trading signal:\nSymbol: {market_data. get('symbol', \n'UNKNOWN')}\nTimeframe: \n{market_data. get('timeframe', \n'UNKNOWN')}\nCurrent Price: \n{market_data. get('current_price', \n'N/A')}\nOrder Blocks:\n- Active Bullish Blocks: \n{market_data. get('bullish_blocks', \n0)}\n- Active Bearish Blocks: \n{market_data. get('bearish_blocks', \n0)}\nFair Value Gaps:\n- Open FVGs: \n{market_data. get('open_fvgs', 0)}\nRecent Price Action:\n{market_data. get('recent_candles', \n'No data')}\nBased on Smart Money Concepts and \nOrder Block theory, provide:\n1."}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 14:", "output": "get('recent_candles', \n'No data')}\nBased on Smart Money Concepts and \nOrder Block theory, provide:\n1. Signal Direction: BULLISH, \nBEARISH, or NEUTRAL\n2. Confidence Level: 0-100%\n3. Brief Reason: Why this signal?\nFormat your response as:\nSIGNAL: [BULLISH/BEARISH/NEUTRAL]\nCONFIDENCE: [0-100]\nREASON: [Brief explanation]\n\"\"\"\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 def _parse_response(self, \nresponse_text: str) -> Dict:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"\"\"Parse AI response into \nstructured format\"\"\"\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 signal = {\"direction\": 0, \n\"confidence\": 0, \"reason\": \"\"}\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 try:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 lines = \nresponse_text. strip(). split('\\n')\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 for line in lines:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 if 'SIGNAL:' in \nline. upper():\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 if 'BULLISH' in \nline. upper():\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \nsignal['direction'] = 1\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 elif 'BEARISH' in \nline."}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 15:", "output": "upper():\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \nsignal['direction'] = 1\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 elif 'BEARISH' in \nline. upper():\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \nsignal['direction'] = -1\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 else:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \nsignal['direction'] = 0\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 elif 'CONFIDENCE:' in \nline. upper():\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 confidence_str = \n''. join(filter(str. isdigit, line))\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \nsignal['confidence'] = \nint(confidence_str) if confidence_str \nelse 0\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 elif 'REASON:' in \nline. upper():\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 signal['reason'] \n= line. split('REASON:')[1]. strip()\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 except Exception as e:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(f\"Error parsing \nresponse: {e}\")\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return signal\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 # OpenAI-compatible providers \n(OpenAI, DeepSeek, Groq, Together, \netc. )"}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 16:", "output": "def _call_openai_compatible(self, \nmarket_data: Dict, url: str, model: \nstr) -> Dict:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"\"\"Call OpenAI-compatible \nAPI\"\"\"\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 headers = {\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"Authorization\": f\"Bearer \n{self. api_key}\",\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"Content-Type\": \n\"application/json\"\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 }\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 payload = {\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"model\": model,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"messages\": [\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 {\"role\": \"system\", \n\"content\": \"You are an expert forex \ntrader specializing in Smart Money \nConcepts. \"},\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 {\"role\": \"user\", \n\"content\": \nself. _create_prompt(market_data)}\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 ],\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"temperature\": 0. 3,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"max_tokens\": 500\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 }\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 response = requests. post(url, \nheaders=headers, json=payload, \ntimeout=30)\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 response. raise_for_status()\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 result = response. json()\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 ai_response = \nresult['choices'][0]['message']\n['content']\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return \nself."}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 17:", "output": "json()\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 ai_response = \nresult['choices'][0]['message']\n['content']\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return \nself. _parse_response(ai_response)\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 def _call_openai(self, \nmarket_data: Dict) -> Dict:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return \nself. _call_openai_compatible(market_d\nata, AIProviderConfig. OPENAI_URL, \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\n\u00a0\u00a0\u00a0\u00a0\u00a0 self. config. get('model', \nAIProviderConfig. OPENAI_MODEL))\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 def _call_deepseek(self, \nmarket_data: Dict) -> Dict:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return \nself. _call_openai_compatible(market_d\nata, AIProviderConfig. DEEPSEEK_URL, \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\n\u00a0\u00a0\u00a0\u00a0\u00a0 self. config. get('model', \nAIProviderConfig. DEEPSEEK_MODEL))\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 def _call_groq(self, market_data: \nDict) -> Dict:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return \nself. _call_openai_compatible(market_d\nata, AIProviderConfig. GROQ_URL, \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\n\u00a0\u00a0\u00a0\u00a0\u00a0 self. config. get('model', \nAIProviderConfig."}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 18:", "output": "GROQ_URL, \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\n\u00a0\u00a0\u00a0\u00a0\u00a0 self. config. get('model', \nAIProviderConfig. GROQ_MODEL))\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 def _call_together(self, \nmarket_data: Dict) -> Dict:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return \nself. _call_openai_compatible(market_d\nata, AIProviderConfig. TOGETHER_URL, \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\n\u00a0\u00a0\u00a0\u00a0\u00a0 self. config. get('model', \nAIProviderConfig. TOGETHER_MODEL))\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 def _call_mistral(self, \nmarket_data: Dict) -> Dict:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return \nself. _call_openai_compatible(market_d\nata, AIProviderConfig. MISTRAL_URL, \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\n\u00a0\u00a0\u00a0\u00a0\u00a0 self. config. get('model', \nAIProviderConfig. MISTRAL_MODEL))\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 def _call_xai(self, market_data: \nDict) -> Dict:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return \nself. _call_openai_compatible(market_d\nata, AIProviderConfig. XAI_URL, \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\n\u00a0\u00a0\u00a0\u00a0\u00a0 self. config."}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 19:", "output": "XAI_URL, \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\n\u00a0\u00a0\u00a0\u00a0\u00a0 self. config. get('model', \nAIProviderConfig. XAI_MODEL))\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 def _call_fireworks(self, \nmarket_data: Dict) -> Dict:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return \nself. _call_openai_compatible(market_d\nata, AIProviderConfig. FIREWORKS_URL, \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\n\u00a0\u00a0\u00a0\u00a0\u00a0 self. config. get('model', \nAIProviderConfig. FIREWORKS_MODEL))\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 def _call_anyscale(self, \nmarket_data: Dict) -> Dict:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return \nself. _call_openai_compatible(market_d\nata, AIProviderConfig. ANYSCALE_URL, \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\n\u00a0\u00a0\u00a0\u00a0\u00a0 self. config. get('model', \nAIProviderConfig. ANYSCALE_MODEL))\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 def _call_ai21(self, market_data: \nDict) -> Dict:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return \nself. _call_openai_compatible(market_d\nata, AIProviderConfig. AI21_URL, \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\n\u00a0\u00a0\u00a0\u00a0\u00a0 self. config. get('model', \nAIProviderConfig."}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 20:", "output": "AI21_URL, \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\n\u00a0\u00a0\u00a0\u00a0\u00a0 self. config. get('model', \nAIProviderConfig. AI21_MODEL))\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 # Anthropic (Claude)\n\u00a0\u00a0\u00a0 def _call_anthropic(self, \nmarket_data: Dict) -> Dict:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 headers = {\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"x-api-key\": \nself. api_key,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"anthropic-version\": \n\"2023-06-01\",\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"Content-Type\": \n\"application/json\"\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 }\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 payload = {\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"model\": \nself. config. get('model', \nAIProviderConfig. ANTHROPIC_MODEL),\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"max_tokens\": 1024,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"messages\": [\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 {\"role\": \"user\", \n\"content\": \nself. _create_prompt(market_data)}\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 ]\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 }\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 response = \nrequests. post(AIProviderConfig. ANTHRO\nPIC_URL, headers=headers, \njson=payload, timeout=30)\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 response. raise_for_status()\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 result = response."}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 21:", "output": "raise_for_status()\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 result = response. json()\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 ai_response = \nresult['content'][0]['text']\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return \nself. _parse_response(ai_response)\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 # Google Gemini\n\u00a0\u00a0\u00a0 def _call_google(self, \nmarket_data: Dict) -> Dict:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 url = f\"\n{AIProviderConfig. GOOGLE_URL}?key=\n{self. api_key}\"\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 payload = {\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"contents\": [{\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"parts\": [{\"text\": \nself. _create_prompt(market_data)}]\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 }]\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 }\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 response = requests. post(url, \njson=payload, timeout=30)\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 response. raise_for_status()\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 result = response. json()\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 ai_response = \nresult['candidates'][0]['content']\n['parts'][0]['text']\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return \nself. _parse_response(ai_response)\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 # Perplexity\n\u00a0\u00a0\u00a0 def _call_perplexity(self, \nmarket_data: Dict) -> Dict:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 headers = {\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"Authorization\": f\"Bearer \n{self."}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 22:", "output": "api_key}\",\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"Content-Type\": \n\"application/json\"\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 }\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 payload = {\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"model\": \nself. config. get('model', \nAIProviderConfig. PERPLEXITY_MODEL),\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"messages\": [\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 {\"role\": \"user\", \n\"content\": \nself. _create_prompt(market_data)}\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 ]\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 }\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 response = \nrequests. post(AIProviderConfig. PERPLE\nXITY_URL, headers=headers, \njson=payload, timeout=30)\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 response. raise_for_status()\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 result = response. json()\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 ai_response = \nresult['choices'][0]['message']\n['content']\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return \nself. _parse_response(ai_response)\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 # Cohere\n\u00a0\u00a0\u00a0 def _call_cohere(self, \nmarket_data: Dict) -> Dict:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 headers = {\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"Authorization\": f\"Bearer \n{self. api_key}\",\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"Content-Type\": \n\"application/json\"\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 }\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 payload = {\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"model\": \nself. config. get('model', \nAIProviderConfig."}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 23:", "output": "config. get('model', \nAIProviderConfig. COHERE_MODEL),\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"message\": \nself. _create_prompt(market_data)\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 }\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 response = \nrequests. post(AIProviderConfig. COHERE\n_URL, headers=headers, json=payload, \ntimeout=30)\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 response. raise_for_status()\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 result = response. json()\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 ai_response = result['text']\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return \nself. _parse_response(ai_response)\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 # HuggingFace\n\u00a0\u00a0\u00a0 def _call_huggingface(self, \nmarket_data: Dict) -> Dict:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 repo = \nself. config. get('repo', \nAIProviderConfig. HUGGINGFACE_REPO)\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 url = f\"https://api-\ninference. huggingface. co/models/{repo\n}\"\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 headers = {\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"Authorization\": f\"Bearer \n{self. api_key}\"\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 }\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 payload = {\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"inputs\": \nself. _create_prompt(market_data)\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 }\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 response = requests. post(url, \nheaders=headers, json=payload, \ntimeout=30)\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 response."}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 24:", "output": "post(url, \nheaders=headers, json=payload, \ntimeout=30)\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 response. raise_for_status()\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 result = response. json()\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 ai_response = result[0]\n['generated_text'] if \nisinstance(result, list) else \nresult. get('generated_text', '')\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return \nself. _parse_response(ai_response)\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 # Local Models - Ollama\n\u00a0\u00a0\u00a0 def _call_ollama(self, \nmarket_data: Dict) -> Dict:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 url = self. config. get('url', \nAIProviderConfig. OLLAMA_URL)\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 payload = {\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"model\": \nself. config. get('model', \nAIProviderConfig. OLLAMA_MODEL),\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"messages\": [\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 {\"role\": \"user\", \n\"content\": \nself. _create_prompt(market_data)}\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 ],\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"stream\": False\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 }\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 response = requests. post(url, \njson=payload, timeout=60)\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 response. raise_for_status()\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 result = response."}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 25:", "output": "post(url, \njson=payload, timeout=60)\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 response. raise_for_status()\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 result = response. json()\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 ai_response = \nresult['message']['content']\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return \nself. _parse_response(ai_response)\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 # LM Studio\n\u00a0\u00a0\u00a0 def _call_lm_studio(self, \nmarket_data: Dict) -> Dict:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return \nself. _call_openai_compatible(market_d\nata, \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\n\u00a0\u00a0\u00a0\u00a0\u00a0 self. config. get('url', \nAIProviderConfig. LM_STUDIO_URL),\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\n\u00a0\u00a0\u00a0\u00a0\u00a0 self. config. get('model', \nAIProviderConfig. LM_STUDIO_MODEL))\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 # GPT4All\n\u00a0\u00a0\u00a0 def _call_gpt4all(self, \nmarket_data: Dict) -> Dict:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return \nself. _call_openai_compatible(market_d\nata, \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\n\u00a0\u00a0\u00a0\u00a0\u00a0 self. config. get('url', \nAIProviderConfig. GPT4ALL_URL),\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\n\u00a0\u00a0\u00a0\u00a0\u00a0 self. config. get('model', \nAIProviderConfig."}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 26:", "output": "GPT4ALL_URL),\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\n\u00a0\u00a0\u00a0\u00a0\u00a0 self. config. get('model', \nAIProviderConfig. GPT4ALL_MODEL))\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 # LocalAI\n\u00a0\u00a0\u00a0 def _call_localai(self, \nmarket_data: Dict) -> Dict:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return \nself. _call_openai_compatible(market_d\nata, \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\n\u00a0\u00a0\u00a0\u00a0\u00a0 self. config. get('url', \nAIProviderConfig. LOCALAI_URL),\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\n\u00a0\u00a0\u00a0\u00a0\u00a0 self. config. get('model', \nAIProviderConfig. LOCALAI_MODEL))\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 # Text-Generation-WebUI \n(Oobabooga)\n\u00a0\u00a0\u00a0 def _call_textgen_webui(self, \nmarket_data: Dict) -> Dict:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return \nself. _call_openai_compatible(market_d\nata, \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\n\u00a0\u00a0\u00a0\u00a0\u00a0 self. config. get('url', \nAIProviderConfig. TEXTGEN_WEBUI_URL),\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\n\u00a0\u00a0\u00a0\u00a0\u00a0 self. config. get('model', \nAIProviderConfig."}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 27:", "output": "TEXTGEN_WEBUI_URL),\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\n\u00a0\u00a0\u00a0\u00a0\u00a0 self. config. get('model', \nAIProviderConfig. TEXTGEN_WEBUI_MODEL)\n)\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 # vLLM\n\u00a0\u00a0\u00a0 def _call_vllm(self, market_data: \nDict) -> Dict:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return \nself. _call_openai_compatible(market_d\nata, \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\n\u00a0\u00a0\u00a0\u00a0\u00a0 self. config. get('url', \nAIProviderConfig. VLLM_URL),\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\n\u00a0\u00a0\u00a0\u00a0\u00a0 self. config. get('model', \nAIProviderConfig. VLLM_MODEL))\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 # KoboldCPP\n\u00a0\u00a0\u00a0 def _call_koboldcpp(self, \nmarket_data: Dict) -> Dict:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 url = self. config. get('url', \nAIProviderConfig. KOBOLDCPP_URL)\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 payload = {\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"prompt\": \nself. _create_prompt(market_data),\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"max_length\": 500,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"temperature\": 0. 3\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 }\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 response = requests."}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 28:", "output": "3\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 }\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 response = requests. post(url, \njson=payload, timeout=60)\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 response. raise_for_status()\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 result = response. json()\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 ai_response = \nresult['results'][0]['text']\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return \nself. _parse_response(ai_response)\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 # llama. cpp\n\u00a0\u00a0\u00a0 def _call_llamacpp(self, \nmarket_data: Dict) -> Dict:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 url = self. config. get('url', \nAIProviderConfig. LLAMACPP_URL)\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 payload = {\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"prompt\": \nself. _create_prompt(market_data),\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"n_predict\": 500,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"temperature\": 0. 3\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 }\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 response = requests. post(url, \njson=payload, timeout=60)\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 response. raise_for_status()\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 result = response. json()\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 ai_response = \nresult['content']\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return \nself."}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 29:", "output": "json()\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 ai_response = \nresult['content']\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return \nself. _parse_response(ai_response)\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 # Placeholder methods for \nproviders requiring more complex \nsetup\n\u00a0\u00a0\u00a0 def _call_replicate(self, \nmarket_data: Dict) -> Dict:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(\"Replicate integration \nrequires additional setup. See \ndocumentation. \")\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return {\"direction\": 0, \n\"confidence\": 0, \"reason\": \"Not \nimplemented\"}\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 def _call_azure(self, \nmarket_data: Dict) -> Dict:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(\"Azure OpenAI \nintegration requires custom endpoint \nconfiguration. \")\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return {\"direction\": 0, \n\"confidence\": 0, \"reason\": \"Not \nimplemented\"}\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 def _call_bedrock(self, \nmarket_data: Dict) -> Dict:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(\"AWS Bedrock \nintegration requires boto3. See \ndocumentation. \")\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return {\"direction\": 0, \n\"confidence\": 0, \"reason\": \"Not \nimplemented\"}\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 def _call_vertex(self, \nmarket_data: Dict) -> Dict:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(\"Google Vertex AI \nintegration requires GCP SDK. See \ndocumentation. \")"}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 30:", "output": "See \ndocumentation. \")\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return {\"direction\": 0, \n\"confidence\": 0, \"reason\": \"Not \nimplemented\"}\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 def _call_cloudflare(self, \nmarket_data: Dict) -> Dict:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(\"Cloudflare AI \nintegration requires account setup. \nSee documentation. \")\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return {\"direction\": 0, \n\"confidence\": 0, \"reason\": \"Not \nimplemented\"}\n#====================================\n=====================================\n====\n# ML DATA PROCESSING (Unchanged from \nv1. 0)\n#====================================\n=====================================\n====\nclass MLDataProcessor:\n\u00a0\u00a0\u00a0 \"\"\"Process EA-exported data for \nML training\"\"\"\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 def __init__(self, data_path: str \n= None):\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 self. data_path = data_path or \nAIProviderConfig. ML_DATA_PATH\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 def load_all_datasets(self) -> \npd. DataFrame:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"\"\"Load all CSV files from ML \ndata directory\"\"\"\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 all_data = []\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 if not \nos. path. exists(self. data_path):\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(f\"Data path not \nfound: {self. data_path}\")\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return pd."}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 31:", "output": "data_path):\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(f\"Data path not \nfound: {self. data_path}\")\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return pd. DataFrame()\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 csv_files = [f for f in \nos. listdir(self. data_path) if \nf. endswith('. csv')]\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(f\"Found \n{len(csv_files)} CSV files\")\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 for file in csv_files:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 filepath = \nos. path. join(self. data_path, file)\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 try:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 df = \npd. read_csv(filepath)\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 all_data. append(df)\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(f\"Loaded: \n{file} ({len(df)} rows)\")\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 except Exception as e:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(f\"Error loading \n{file}: {e}\")\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 if all_data:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 combined_df = \npd. concat(all_data, \nignore_index=True)\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(f\"\\nTotal rows \nloaded: {len(combined_df)}\")\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return combined_df\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 else:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return pd. DataFrame()\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 def prepare_features_labels(self, \ndf: pd. DataFrame) -> \nTuple[np."}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 32:", "output": "DataFrame()\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 def prepare_features_labels(self, \ndf: pd. DataFrame) -> \nTuple[np. ndarray, np. ndarray]:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"\"\"Prepare feature matrix X \nand label vector y\"\"\"\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 feature_cols = [col for col \nin df. columns if \ncol. startswith('Feature')]\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 if len(feature_cols) == 0:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(\"No feature columns \nfound!\")\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return np. array([]), \nnp. array([])\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 X = df[feature_cols]. values\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 y = (df['Result'] > \n0). astype(int). values\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(f\"Features: {X. shape}\")\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(f\"Labels: {y. shape}\")\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(f\"Win rate: \n{np. mean(y):. 2%}\")\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return X, y\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 def clean_data(self, X: \nnp. ndarray, y: np. ndarray) -> \nTuple[np. ndarray, np. ndarray]:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"\"\"Remove NaN and infinite \nvalues\"\"\"\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 valid_mask = ~\n(np. isnan(X)."}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 33:", "output": "ndarray]:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"\"\"Remove NaN and infinite \nvalues\"\"\"\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 valid_mask = ~\n(np. isnan(X). any(axis=1) | \nnp. isinf(X). any(axis=1))\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 X_clean = X[valid_mask]\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 y_clean = y[valid_mask]\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(f\"Removed \n{np. sum(~valid_mask)} invalid rows\")\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(f\"Clean dataset: \n{X_clean. shape[0]} samples\")\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return X_clean, y_clean\n#====================================\n=====================================\n====\n# ML TRAINING (Unchanged from v1. 0)\n#====================================\n=====================================\n====\nclass MLTrainer:\n\u00a0\u00a0\u00a0 \"\"\"Train and evaluate ML models \nfor trade prediction\"\"\"\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 def __init__(self):\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 self. scaler = \nStandardScaler()\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 self. model = None\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 self. model_type = None\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 def train_random_forest(self, \nX_train, y_train, n_estimators=100):\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"\"\"Train Random Forest \nclassifier\"\"\"\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(\"\\n=== Training Random \nForest ===\")\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 self."}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 34:", "output": "model = \nRandomForestClassifier(\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \nn_estimators=n_estimators,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 max_depth=10,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 min_samples_split=20,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 min_samples_leaf=10,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \nrandom_state=AIProviderConfig. RANDOM_\nSTATE,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 n_jobs=-1\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 )\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 self. model. fit(X_train, \ny_train)\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 self. model_type = \n\"RandomForest\"\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(\"Training complete!\")\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 def train_gradient_boosting(self, \nX_train, y_train, n_estimators=100):\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"\"\"Train Gradient Boosting \nclassifier\"\"\"\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(\"\\n=== Training \nGradient Boosting ===\")\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 self. model = \nGradientBoostingClassifier(\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \nn_estimators=n_estimators,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 learning_rate=0. 1,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 max_depth=5,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 min_samples_split=20,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 min_samples_leaf=10,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \nrandom_state=AIProviderConfig. RANDOM_\nSTATE\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 )\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 self. model. fit(X_train, \ny_train)\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 self."}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 35:", "output": "RANDOM_\nSTATE\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 )\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 self. model. fit(X_train, \ny_train)\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 self. model_type = \n\"GradientBoosting\"\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(\"Training complete!\")\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 def evaluate(self, X_test, \ny_test):\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"\"\"Evaluate model \nperformance\"\"\"\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 if self. model is None:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(\"No model trained \nyet!\")\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(\"\\n=== Model Evaluation \n===\")\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 y_pred = \nself. model. predict(X_test)\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 y_proba = \nself. model. predict_proba(X_test)\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 accuracy = np. mean(y_pred == \ny_test)\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(f\"Accuracy: \n{accuracy:."}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 36:", "output": "mean(y_pred == \ny_test)\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(f\"Accuracy: \n{accuracy:. 2%}\")\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(\"\\nClassification \nReport:\")\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \nprint(classification_report(y_test, \ny_pred, target_names=['Loss', \n'Win']))\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(\"\\nConfusion Matrix:\")\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 cm = confusion_matrix(y_test, \ny_pred)\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(cm)\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(f\"\u00a0 [[TN={cm[0,0]} FP=\n{cm[0,1]}]\")\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(f\"\u00a0\u00a0 [FN={cm[1,0]} TP=\n{cm[1,1]}]]\")\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 if hasattr(self. model, \n'feature_importances_'):\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(\"\\nTop 10 Most \nImportant Features:\")\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 importances = \nself. model. feature_importances_\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 indices = \nnp. argsort(importances)[::-1][:10]\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 for i, idx in \nenumerate(indices):\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(f\"\u00a0 {i+1}. \nFeature{idx}: \n{importances[idx]:."}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 37:", "output": "Feature{idx}: \n{importances[idx]:. 4f}\")\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return accuracy\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 def cross_validate(self, X, y, \ncv=5):\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"\"\"Perform cross-\nvalidation\"\"\"\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 if self. model is None:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(\"No model trained \nyet!\")\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(f\"\\n=== {cv}-Fold \nCross-Validation ===\")\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 scores = \ncross_val_score(self. model, X, y, \ncv=cv, scoring='accuracy')\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(f\"Scores: {scores}\")\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(f\"Mean: \n{np. mean(scores):. 2%}\")\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(f\"Std: \n{np. std(scores):. 2%}\")\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return scores\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 def save_model(self, filepath: \nstr = None):\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"\"\"Save trained model to \ndisk\"\"\"\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 if self. model is None:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(\"No model to \nsave!\")\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 if filepath is None:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 timestamp = \ndatetime. now(). strftime(\"%Y%m%d_%H%M%\nS\")\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 filepath = \nos."}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 38:", "output": "now(). strftime(\"%Y%m%d_%H%M%\nS\")\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 filepath = \nos. path. join(AIProviderConfig. MODEL_O\nUTPUT_PATH, \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \nf\"model_{self. model_type}_{timestamp}\n. pkl\")\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \nos. makedirs(os. path. dirname(filepath)\n, exist_ok=True)\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 joblib. dump({\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 'model': self. model,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 'scaler': self. scaler,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 'model_type': \nself. model_type\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 }, filepath)\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(f\"Model saved: \n{filepath}\")\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 def load_model(self, filepath: \nstr):\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"\"\"Load trained model from \ndisk\"\"\"\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 if not \nos. path. exists(filepath):\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(f\"Model file not \nfound: {filepath}\")\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 data = joblib. load(filepath)\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 self. model = data['model']\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 self. scaler = data['scaler']\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 self."}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 39:", "output": "load(filepath)\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 self. model = data['model']\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 self. scaler = data['scaler']\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 self. model_type = \ndata['model_type']\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(f\"Model loaded: \n{self. model_type}\")\n#====================================\n=====================================\n====\n# COMPLETE TRAINING PIPELINE\n#====================================\n=====================================\n====\ndef train_complete_pipeline():\n\u00a0\u00a0\u00a0 \"\"\"Complete ML training \npipeline\"\"\"\n\u00a0\u00a0\u00a0 print(\"=\"*60)\n\u00a0\u00a0\u00a0 print(\"ORDER BLOCK SCALPER - ML \nTRAINING PIPELINE\")\n\u00a0\u00a0\u00a0 print(\"=\"*60)\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 processor = MLDataProcessor()\n\u00a0\u00a0\u00a0 df = \nprocessor. load_all_datasets()\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 if df. empty:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(\"No data found! Export \ndata from EA first. \")\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 X, y = \nprocessor. prepare_features_labels(df)\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 if X. shape[0] == 0:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(\"No valid data to train \non!\")\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 return\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 X, y = processor."}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 40:", "output": "return\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 X, y = processor. clean_data(X, y)\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 X_train, X_test, y_train, y_test \n= train_test_split(\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 X, y, \ntest_size=AIProviderConfig. TEST_SIZE, \nrandom_state=AIProviderConfig. RANDOM_\nSTATE\n\u00a0\u00a0\u00a0 )\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 print(f\"\\nTrain set: \n{X_train. shape[0]} samples\")\n\u00a0\u00a0\u00a0 print(f\"Test set: \n{X_test. shape[0]} samples\")\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 trainer = MLTrainer()\n\u00a0\u00a0\u00a0 X_train_scaled = \ntrainer. scaler. fit_transform(X_train)\n\u00a0\u00a0\u00a0 X_test_scaled = \ntrainer. scaler. transform(X_test)\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 \ntrainer. train_random_forest(X_train_s\ncaled, y_train, n_estimators=200)\n\u00a0\u00a0\u00a0 accuracy_rf = \ntrainer. evaluate(X_test_scaled, \ny_test)\n\u00a0\u00a0\u00a0 \ntrainer. cross_validate(X_train_scaled\n, y_train, \ncv=AIProviderConfig. CV_FOLDS)\n\u00a0\u00a0\u00a0 trainer. save_model()\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 trainer2 = MLTrainer()\n\u00a0\u00a0\u00a0 trainer2. scaler = trainer."}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 41:", "output": "save_model()\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 trainer2 = MLTrainer()\n\u00a0\u00a0\u00a0 trainer2. scaler = trainer. scaler\n\u00a0\u00a0\u00a0 \ntrainer2. train_gradient_boosting(X_tr\nain_scaled, y_train, \nn_estimators=200)\n\u00a0\u00a0\u00a0 accuracy_gb = \ntrainer2. evaluate(X_test_scaled, \ny_test)\n\u00a0\u00a0\u00a0 \ntrainer2. cross_validate(X_train_scale\nd, y_train, \ncv=AIProviderConfig. CV_FOLDS)\n\u00a0\u00a0\u00a0 trainer2. save_model()\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 print(\"\\n\" + \"=\"*60)\n\u00a0\u00a0\u00a0 print(\"TRAINING COMPLETE!\")\n\u00a0\u00a0\u00a0 print(\"=\"*60)\n\u00a0\u00a0\u00a0 print(f\"Random Forest Accuracy: \n{accuracy_rf:. 2%}\")\n\u00a0\u00a0\u00a0 print(f\"Gradient Boosting \nAccuracy: {accuracy_gb:. 2%}\")\n\u00a0\u00a0\u00a0 print(\"\\nModels saved to:\", \nAIProviderConfig."}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 42:", "output": "2%}\")\n\u00a0\u00a0\u00a0 print(\"\\nModels saved to:\", \nAIProviderConfig. MODEL_OUTPUT_PATH)\n\u00a0\u00a0\u00a0 print(\"=\"*60)\n#====================================\n=====================================\n====\n# EXAMPLE USAGE\n#====================================\n=====================================\n====\ndef example_ai_analysis(provider: str \n= \"OpenAI\"):\n\u00a0\u00a0\u00a0 \"\"\"Example: Using AI for market \nanalysis\"\"\"\n\u00a0\u00a0\u00a0 print(f\"\\n=== AI Market Analysis \nExample ({provider}) ===\\n\")\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 market_data = {\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 'symbol': 'EURUSD',\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 'timeframe': 'M15',\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 'current_price': 1. 09450,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 'bullish_blocks': 2,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 'bearish_blocks': 1,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 'open_fvgs': 3,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 'recent_candles': \"\"\"\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 [1] O:1. 0945 H:1. 0948 \nL:1. 0942 C:1. 0946\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 [2] O:1. 0946 H:1. 0950 \nL:1. 0944 C:1. 0949\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 [3] O:1. 0949 H:1. 0952 \nL:1. 0947 C:1."}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 43:", "output": "0949 H:1. 0952 \nL:1. 0947 C:1. 0948\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \"\"\",\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 'market_structure': 'Price \nbroke previous high, potential \nbullish continuation'\n\u00a0\u00a0\u00a0 }\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 client = \nUniversalAIClient(provider=provider)\n\u00a0\u00a0\u00a0 signal = \nclient."}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 44:", "output": "analyze_market(market_data)\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 print(f\"\\nProvider: {provider}\")\n\u00a0\u00a0\u00a0 print(f\"Signal: \n{signal['direction']} (1=Bull, \n-1=Bear, 0=Neutral)\")\n\u00a0\u00a0\u00a0 print(f\"Confidence: \n{signal['confidence']}%\")\n\u00a0\u00a0\u00a0 print(f\"Reason: \n{signal['reason']}\")\n#====================================\n=====================================\n====\n# MAIN EXECUTION\n#====================================\n=====================================\n====\nif __name__ == \"__main__\":\n\u00a0\u00a0\u00a0 print(\"\"\"\n\u2554 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550\n\u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550\n\u2550 \u2550 \u2550 \u2557\n\u2551\u00a0\u00a0 ORDER BLOCK SCALPER - AI & ML \nSUITE v2."}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 45:", "output": "0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u2551\n\u2551\u00a0\u00a0 Supports 20+ AI Providers + Local \nModels\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u2551\n\u255a \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550\n\u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550 \u2550\n\u2550 \u2550 \u2550 \u255d\nSelect an option:\n1. Train ML Models from EA Data\n2. Test AI Analysis (OpenAI)\n3. Test AI Analysis (Anthropic \nClaude)\n4. Test AI Analysis (Google Gemini)\n5. Test AI Analysis (DeepSeek)\n6. Test AI Analysis (Local - Ollama)\n7. Make Prediction with Trained Model\n8."}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 46:", "output": "Test AI Analysis (Local - Ollama)\n7. Make Prediction with Trained Model\n8. Exit\n\u00a0\u00a0\u00a0 \"\"\")\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 choice = input(\"Enter choice (1-\n8): \"). strip()\n\u00a0\u00a0\u00a0 \n\u00a0\u00a0\u00a0 if choice == \"1\":\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 train_complete_pipeline()\n\u00a0\u00a0\u00a0 elif choice == \"2\":\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 example_ai_analysis(\"OpenAI\")\n\u00a0\u00a0\u00a0 elif choice == \"3\":\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \nexample_ai_analysis(\"Anthropic\")\n\u00a0\u00a0\u00a0 elif choice == \"4\":\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 example_ai_analysis(\"Google\")\n\u00a0\u00a0\u00a0 elif choice == \"5\":\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \nexample_ai_analysis(\"DeepSeek\")\n\u00a0\u00a0\u00a0 elif choice == \"6\":\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 example_ai_analysis(\"Ollama\")\n\u00a0\u00a0\u00a0 elif choice == \"7\":\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(\"See \nexample_ml_prediction() function in \ncode\")\n\u00a0\u00a0\u00a0 elif choice == \"8\":\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(\"Goodbye!\")\n\u00a0\u00a0\u00a0 else:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(\"Invalid choice!\")\n\n--- FILE SPLIT ---\n\nICT Liquidity Pool \u2013 Trade Like a Pro \nBeing an ICT trader do you want to learn about ICT liquidity pool in depth? \nWell, in this blog post we will teach you everything about ICT liquidity pool from its \ndefinition to identification and to its use in trading with examples. \nNow lets start with defining the ICT liquidity pool."}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 47:", "output": "Now lets start with defining the ICT liquidity pool. \nWhat is ICT Liquidity Pool? \nTo understand ICT liquidity pool first you have to learn about Liquidity . Liquidity means \nthe willing buyer and sellers who want to sell/buy at market price. \nMostly the retail traders buy or sell at market price while the smart money buys below \nmarket price and sells above the market price. \nYou have to look where the most retail traders be willing to buy or sell. Mostly they will \nbe buying above an old high after breakout and selling below the old low as a breakout \ntrade. \nOr if they already bought they will put their sell stop below old low to mitigate their loss \nand if they already sold they will put their buy stop above an old high. \nSo the old highs have a pool of liquidity resting above them in the form of buy stops and \nthe wiling buyers to buy after breakout. \nAnd old lows have a pool of liquidity resting below them in the form of sell stops or \nwilling sellers at market price after breakout of low. \n \n\nThat is why the area above an old high and area below an old low are termed as ICT \nliquidity pool."}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 48:", "output": "That is why the area above an old high and area below an old low are termed as ICT \nliquidity pool. \nAnd smart money / ICT traders look to trade these liquidity pools against retail traders. \nBullish ICT Liquidity Poll \nBullish liquidity pools are areas on the price chart where smart money traders look to \nbuy. \nAs we discussed earlier below the old lows sell side liquidity rests in the form of sell \nstops. \nSo in bullish trend you will look for Discount Zone to buy when price goes below an old \nlow clearing the sell side liquidity. \nIn this case you will be buying against the sell orders of retail traders and price will most \nlikely go higher to target buy side liquidity. \n \nYour stop loss in this scenario will not be more than 40/50 pips as price should not go \nmuch lower below an old low. \nFor take profit you will target the old highs as buy side liquidity. \nBearish ICT Liquidity Poll \nBearish liquidity pools are areas on the price chart where smart money traders look to \nsell. \n\nAs we discussed earlier below the old highs buy side liquidity rests in the form of buy \nstops."}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 49:", "output": "As we discussed earlier below the old highs buy side liquidity rests in the form of buy \nstops. \nSo in bearish trend you will look for Premium Zone to sell when price goes above an old \nhigh clearing the buy side liquidity. \nIn this case you will be selling against the buy orders of retail traders and price will most \nlikely go lower to target sell side liquidity. \n \nYour stop loss in this scenario will not be more than 40/50 pips as price should not go \nmuch higher above an old high. \nFor take profit you will target the old lows as sell side liquidity. \nDo We Need Confirmation to Trade ICT Liquidity Pool? \nNo you do not need any confirmation while executing trade using ICT liquidity pool. \nYou just need to set a 40/50 pips stop loss because price just seek liquidity and returns \nafter targeting the highs or lows. \nHow Much We should Risk per Trade? \nYou should risk only that amount which you can afford to lose. \nStandardly you should risk only 1% of your capital per trade. \nIs ICT Liquidity Pool a Reliable Model? \nYes it is one of the reliable trading model from ICT teachings."}
{"instruction": "Analyze the chronological sequence of market data parameters to establish structural setup context.", "input": "Market Narrative Segment 50:", "output": "Is ICT Liquidity Pool a Reliable Model? \nYes it is one of the reliable trading model from ICT teachings. But to get better results \nyou should have a correct direction and bias of the market."}