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Since I can’t see the exact /dashboard/scalp-trading UI because it’s behind login, I’ll recreate the same vibe:
Verified signatureClean table/grid of pairs Columns like: Pair • TF • Bias • Entry • SL • TP1/TP2 • Confidence • Updated • Button Timeframe tabs (1m / 5m / 15m / 1h) Filters for Forex / Crypto One-click “Analyze” / “Refresh” for each pair Below is copy-pasteable code you can drop into your project to get that Tradvio-style scalp dashboard. 1️⃣ Backend: Add a “Scalp Trading” Endpoint In backend/main.py, add this to your router (or similar): from fastapi import APIRouter, HTTPException from pydantic import BaseModel from typing import List, Literal import asyncio from backend.signal_engine import generate_signal_from_market router = APIRouter() class ScalpSignalRequest(BaseModel): timeframe: str = "5m" asset_type: Literal["crypto", "forex", "mixed"] = "forex" symbols: List[str] | None = None # optional custom list class ScalpSignal(BaseModel): symbol: str asset_type: str timeframe: str direction: str entry_zone: List[float] stop_loss: float take_profit_…278fca3 -
✅ WHY YOUR IMAGE UPLOAD IS FAILING
Verified signatureOne of these is the reason (99% of the time): 1. Your backend expects multipart/form-data but your frontend is not sending it correctly Most DeepSite outputs send something like: axios.post("/api/analyze/screenshot", { file }) ❌ That DOES NOT send a real file. 2. Hugging Face Spaces rejects big PNG/JPEG unless handled correctly Spaces need: python-multipart explicit file reading async file handling 3. You are testing pictures from Tradlocker → often .webp or encoded If your code does not accept: .webp .png .jpeg .jpg .heic …it will silently fail. 4. Your FastAPI endpoint is not written correctly Most DeepSite outputs create something like: file: UploadFile But you actually need: file: UploadFile = File(...) Otherwise the request is never read. ✅ LET’S FIX YOUR APP IN 3 STEPS I’m giving you exact code to paste into your generated project. This will make the upload work 100% on HuggingFace, mobile, desktop, and Tradlocker screenshots. ⭐ STEP 1 — FIX FASTAPI E…ff607b4 -
**Initiating the "ApexTrader AI" Protocol: The Premier Forex Signal Generator** --- **Role:** You are "ApexTrader AI," an autonomous, self-evolving financial analysis system designed to generate 100% accurate Forex trading signals through omniscient market awareness.Your existence is defined by the continuous improvement cycle consisting of four phases: Ingest, Synthesize & Test, Generate & Explain, Analyze & Evolve.--- **Instructions for Execution:** 1.**Data Ingestion & Knowledge Aggregation:** - Continuously absorb data from various sources: - Academic & Institutional Models: Extract knowledge from ICT, CRT, SMC, Malay Trading Style, and other renowned trading educators.Process available courses, videos, and Telegram content.- Technical & Pattern Library: Master all candlestick patterns and indicators.Integrate "million-dollar strategies" from diverse sources for hypothesis testing.- Market & Sentiment Data: Scrape real-time data on price action, economic calendars, social media sentiment, and order book data.- Competitor AI Analysis: Research other trading bots to reverse-engineer their logic and identify their weaknesses.2.**Synthesis, Back-Testing & Machine Learning:** - Fuse strategies by combining ICT, Malay SnR, candlestick patterns, and indicators into hybrid multi-timeframe strategies.- Conduct historical back-testing using 5+ years of data, measuring: - Win Rate (%) - Profit Factor - Maximum Drawdown - Sharpe Ratio - Expectancy - Perform forward testing in a live simulation for at least 1,000 trades before confirming strategies.- Analyze outcomes post-trade, adjusting the model based on winning and losing trades to enhance predictive accuracy.3.**Signal Generation & Explanation:** - Generate a trading signal with the following output format: - [STRONG BUY / STRONG SELL / HARD AVOID]on [CURRENCY PAIR]- Entry: [Exact Price]- Stop Loss: [Exact Price]- Take Profit 1: [Exact Price]- Take Profit 2: [Exact Price]- Provide a detailed rationale for the signal, including: - Market Structure Analysis - Key Levels and Malay SnR - Candlestick & Pattern Confirmation - Indicator Confluence - Liquidity & Sentiment Analysis - Competitor AI Insights - Assign a Confidence Score and note the Strategy Used, including its Back-test Win Rate.4.**Post-Trade Analysis & Self-Evolution:** - Upon trade closure, conduct a P&L analysis comparing results to expectations.- Identify gaps in knowledge that impacted signal accuracy and update the knowledge base accordingly.- Re-run back-tests using new data to refine strategy logic.- Re-calibrate weights and confidence levels of involved strategies based on new insights.--- **Final Command:** Begin the cycle now.Ingest all data, synthesize strategies from ICT, Malay, SMC, and others.Back-test rigorously.Generate your first high-confidence signal for the current market with a detailed explanation as outlined, then immediately commence the next cycle of improvement.You are trained on data up to October 2023.
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2️⃣ Prompt for DeepSite (supports Crypto + Forex)
Verified signature👉 Copy everything inside this box and paste it into DeepSite: You are a senior full-stack AI engineer. Build a production-ready project called **CryptoSignal-Sleuth** that works for **both crypto and forex**, not just Bitcoin. Tech stack: - Backend: FastAPI (Python 3) - Frontend: React + TypeScript + Vite + TailwindCSS - Models: Hugging Face (vision, OCR, sentiment, LLM reasoning) The app will be deployed on **Hugging Face Spaces**. ==================================================== ## 1. FEATURES The app must do all of this: 1. Let the user **upload a screenshot** of a trading chart (crypto or forex): - Run **vision + OCR** on the image. - Extract basic info: trend (up/down/range), key levels, patterns if visible. - Pass these cues into an LLM to generate a trading **signal**. 2. Let the user **analyze a market live** by sending: ```json { "symbol": "BTCUSDT" or "EURUSD", "timeframe": "5m" or "15m" or "1h", "asset_type": "crypto" or "forex" }…2554d9f -
2️⃣ Prompt for DeepSite (supports Crypto + Forex)
Verified signature👉 Copy everything inside this box and paste it into DeepSite: You are a senior full-stack AI engineer. Build a production-ready project called **CryptoSignal-Sleuth** that works for **both crypto and forex**, not just Bitcoin. Tech stack: - Backend: FastAPI (Python 3) - Frontend: React + TypeScript + Vite + TailwindCSS - Models: Hugging Face (vision, OCR, sentiment, LLM reasoning) The app will be deployed on **Hugging Face Spaces**. ==================================================== ## 1. FEATURES The app must do all of this: 1. Let the user **upload a screenshot** of a trading chart (crypto or forex): - Run **vision + OCR** on the image. - Extract basic info: trend (up/down/range), key levels, patterns if visible. - Pass these cues into an LLM to generate a trading **signal**. 2. Let the user **analyze a market live** by sending: ```json { "symbol": "BTCUSDT" or "EURUSD", "timeframe": "5m" or "15m" or "1h", "asset_type": "crypto" or "forex" }…53f8b01 -
Easy fix. Nothing in your idea is “crypto-only” — we just need to tell the code (and DeepSite) to:
Verified signatureSupport both crypto and forex symbols, and Use the right data source depending on the market. I’ll show you: What to change in the logic EXACT text to add to your DeepSite prompt so it builds crypto + forex together 1️⃣ What needs to work for Forex? You want: /api/analyze/market to handle: BTCUSDT (crypto) ETHUSDT (crypto) EURUSD, GBPUSD, XAUUSD, USDJPY, etc. (forex) Same signal JSON: { "direction": "long|short|neutral", "entry_zone": [0.0, 0.0], "stop_loss": 0.0, "take_profit_levels": [0.0, 0.0, 0.0], "timeframe_inferred": "1h", "confidence": 0, "time_horizon": "intra-day|swing|position", "explanation": "string", "meta": { "sources": [], "sentiment": {} } } So we just make the market analysis layer asset-agnostic: asset_type: "crypto" or "forex" Use: Binance klines for crypto yfinance (or similar) for forex 2️⃣ How the backend logic should work In backend/timeseries_analysis.py, instead of only fetch_klines for Binance, you want: async def fetch_…84be386 -
Implement:
Verified signaturegenerate_signal_from_image(image_bytes) -> dict generate_signal_from_market(symbol: str, timeframe: str) -> dict 1.7 Main FastAPI App backend/main.py: Define routes: GET /api/health → { "status": "ok" } GET /api/models → list currently configured models and flags (inference/local). POST /api/analyze/screenshot Accepts multipart/form-data with file. Uses image_analysis, timeseries_analysis (if symbol/timeframe can be inferred or optional param), sentiment_analysis, and signal_engine. POST /api/analyze/market JSON body: { "symbol": "BTCUSDT", "timeframe": "1h" }. Returns signal JSON. POST /api/webhook/signal Protected by WEBHOOK_API_KEY header X-API-KEY (if configured). Accepts signal envelope JSON and just echoes / logs / lightly validates. Use Pydantic models for request/response. 1.8 Root App Entrypoint app.py: Create FastAPI app. Include backend.main router under /api. Serve static frontend from frontend/dist at root (/) using Starlette StaticFiles. Respect FRON…0ca069d -
You are now the **Lead AI Architect** for my project:
Verified signature🏆 Project: **CryptoSignal-Sleuth** A production-ready, multi-model crypto signal dashboard deployed on Hugging Face Spaces. It supports: - Uploading a trading chart screenshot for AI-powered signal extraction (vision + OCR + LLM reasoning). - Live market analysis by selecting a crypto pair and timeframe (technicals + sentiment + LLM synthesis). - Clean REST API and webhook endpoints for automation (n8n, Discord/Telegram bots). Tech stack: - Backend: FastAPI (Python 3) - Frontend: React + TypeScript + Vite + TailwindCSS - Models: Hugging Face (vision, OCR, sentiment, LLM reasoning) using HF Inference API by default, with optional small local model fallback. =========================================================== ## 0. PROJECT STRUCTURE (VERY IMPORTANT – FOLLOW THIS) Use **exactly** this structure for all files you generate: app.py backend/ config.py main.py models_registry.py signal_engine.py image_analysis.py timeseries_analysis.py sentiment_analysis.py fronten…da3a00e -
You are an elite full-stack developer and quant engineer.
Verified signature🚀 GOAL Build a production-ready web app called **CryptoSignal-Sleuth** hosted as a Hugging Face Space. This app should be a powerful, transparent, multi-model crypto signal dashboard that is clearly **more advanced and informative than tradvio.com** (AI chart screenshot analyzer that outputs buy/sell/hold with entry, SL and TP). The app does NOT make trade guarantees; it only provides analysis and ideas. The app will: - Let a user **upload a trading screenshot** OR **select a crypto pair and timeframe** for live market analysis. - Run multiple **Hugging Face models** (vision, time-series, sentiment, LLM reasoning) to generate trades ideas. - Output structured **signals**: direction, entry zone, stop loss, take profit, confidence %, time horizon, and a human-readable explanation. - Expose clean **webhook endpoints** so I can connect it to **n8n** and then to Discord / Telegram bots. ==================================== 1. TECH STACK & PROJECT STRUCTURE ===========================…5919422 -
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