Rajesh Kumar Nait / Perfil
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En mi rol actual, me enfoco en negociar $SOL diariamente como trader de día y ofrezco programas de vanguardia en venta en mi página de vendedor. Estos programas incluyen utilidades de conexión nativas a intercambios de criptomonedas a través de Websocket y la carga de gráficos de todos los intercambios populares mediante Websocket y API. Funcionan perfectamente en VPS, sin necesidad de DLL externos.
Si estás interesado en herramientas de análisis o trading de criptomonedas, no dudes en contactarme por mensaje directo. Echa un vistazo a mis productos criptográficos e integraciones de intercambio en mi página de vendedor de MQL5. Ofrezco soporte durante 12 horas al día en el huso horario IST, los 7 días de la semana.
Por tiempo limitado, todos los productos están disponibles con un descuento del 30%. ¡No te lo pierdas!












This is counterintuitive, but there are solid market microstructure reasons why this happens:
🔍 1. Liquidity Vacuum / Stop-Hunt Trigger
When a market rapidly moves up on low volume, it often means:
Liquidity was thin on the sell side.
A few aggressive buyers cleared the sell wall, triggering:
Stop-losses from shorts (adding buy pressure).
Limit orders above recent lows (creating a vacuum effect).
Result: Price jumps quickly, but volume is still low because not many contracts needed to be traded to move price.
🧠 Think of it like this:
"Price moves the fastest when nobody is willing to sell anymore — not when everyone's buying."
🔍 2. Passive Sellers Withdrew Orders
Another reason: Market makers pulled liquidity because the prior candles looked weak, and they didn’t want to get caught in a reversal.
With fewer passive limit sell orders in the book, even small buy orders push price up more.
This shows up as big candles but low volume.
🔍 3. False Sense of Demand – It’s Not Strength (Yet)
Sometimes big candles on low volume are fake breakouts or traps:
It looks strong, but it’s mostly short covering or bot-driven.
If buyers don’t step in after that candle, it may reverse again — so you want to see confirmation with volume after the big move.






Pionex API EA Connector para MT5 – Integración Perfecta con MT5 Descripción General El Pionex API EA Connector para MT5 permite una integración fluida entre MetaTrader 5 (MT5) y Pionex API . Esta herramienta avanzada permite a los traders ejecutar y gestionar órdenes, obtener información sobre el saldo y hacer seguimiento del historial de operaciones, todo directamente desde MT5 . Funciones Principales 🔹 Gestión de Cuenta y Saldo Get_Balance(); – Obtiene el saldo actual de la cuenta en Pionex
Pionex Live MT5 Data and History -- Agrega la dirección de la API en Herramientas > Asesor Experto api.pionex.com ws.pionex.com Pasos: Crear símbolos Selecciona CreateSymbols = true Reinicia el terminal de MT5 ( muy importante ) Selecciona los símbolos en Market Watch para cargar datos históricos y en vivo Modos disponibles: LiveUpdate – Agrega la utilidad al gráfico para recibir datos en tiempo real History – Agrega la utilidad para cargar el historial hasta la fecha y hora deseadas

1. Learn to adapt as per situation
2. Never Be Determined

1. News and Fundamental analysis impact market but it also go against technical analysis or price action
2. Using an indicator can make you successful
3. Fibonacci calculations does not work
4. You do not need custom analysis assistant build on mt5 or other program before you do analysis
5. You can trade and be successful from smartphone or tablet or promotion of portability in trading.


1 year ago i was enjoying this song, my favorite

1. Running Trading & Charting Software
Most trading platforms (e.g., TradingView, NinjaTrader, MetaTrader, ThinkorSwim) rely more on CPU, but they also use GPUs for rendering charts and handling multiple monitors. GTX 970 can:
Smoothly run multiple charts and indicators.
Handle multiple monitors (good for multi-timeframe analysis).
Speed up heatmaps and visualizations of market data.
2. Accelerating Data Analysis & Backtesting
If I am using Python for market research, I can use NVIDIA’s CUDA for faster computations.
Backtesting libraries: I may Use Backtrader or Zipline with GPU acceleration for strategy testing.
Data analysis: Use cuDF (GPU-accelerated pandas) to handle large market datasets faster.
Machine learning: If you're training AI models for price prediction, NVIDIA’s TensorFlow with CUDA can offload computations to your GPU.
3. Running AI/ML Models for Market Prediction
Even though the GTX 970 is older, it supports TensorFlow/PyTorch (CUDA 11.x max) for basic ML models.
I can train simple neural networks for pattern recognition in stock price movements.
Use libraries like TA-Lib (technical analysis indicators) along with GPU-accelerated data processing.
4. GPU-Accelerated Quantitative Research
Monte Carlo simulations: Speed up simulations for option pricing or risk analysis.
Reinforcement Learning (RL): Experiment with Deep Q-Learning (DQN) for algo trading.
Order Book Analysis: Use GPU-accelerated tools for processing high-frequency trading (HFT) data.
5. Cryptocurrency & Alternative Markets
If I trade crypto, GPU acceleration helps in on-chain analysis, sentiment analysis, or even mining
Some platforms like Cryptohopper or 3Commas allow GPU-based automation for backtesting trading bots.
Limitations of GTX 970 for Trading
✅ Good for visualization, backtesting, and basic AI research.
❌ Not great for heavy deep learning or handling massive datasets.
❌ Limited VRAM (4GB) makes it unsuitable for high-end models.



