Rajesh Kumar Nait / Profil
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3 Jahre
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32
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In meiner aktuellen Rolle konzentriere ich mich darauf, täglich $SOL als Daytrader zu handeln und hochmoderne Programme zum Verkauf auf meiner Verkäuferseite anzubieten. Diese Programme umfassen native WebSocket-Krypto-Börsenverbindungsdienstprogramme und laden Diagramme von allen gängigen Börsen über WebSocket und API. Sie laufen nahtlos auf VPS und benötigen keine externen DLLs.
Wenn Sie an der Analyse von Kryptowährungsdiagrammen oder Handelstools interessiert sind, können Sie mich gerne per DM kontaktieren. Überprüfen Sie meine Kryptoprodukte und Börsenintegrationen auf meiner MQL5-Verkäuferseite. Ich biete 12 Stunden Support pro Tag in der IST-Zeitzone an, 7 Tage die Woche.
Alle Produkte sind für einen begrenzten Zeitraum mit einem Rabatt von 30% erhältlich. Verpassen Sie nicht die Gelegenheit!






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 für MT5 – Nahtlose MT5-Integration Übersicht Der Pionex API EA Connector für MT5 ermöglicht eine nahtlose Integration zwischen MetaTrader 5 (MT5) und der Pionex API . Mit diesem leistungsstarken Tool können Händler direkt von MT5 aus Trades ausführen und verwalten, Kontostände abrufen und die Orderhistorie nachverfolgen. Hauptfunktionen 🔹 Kontoverwaltung & Guthaben Get_Balance(); – Ruft das aktuelle Kontoguthaben bei Pionex ab. 🔹 Orderausführung & -verwaltung
Pionex Live MT5 Data and History -- API-Adresse in Extras > Expert Advisor hinzufügen api.pionex.com ws.pionex.com Schritte: Symbole erstellen CreateSymbols = true setzen MT5-Terminal neu starten ( sehr wichtig! ) Wähle Symbole im Marktüberblick , um historische und Live-Daten zu laden Modi: LiveUpdate – Füge das Tool zum Chart hinzu, um Handelsdaten zu erhalten History – Füge das Tool hinzu, um die Historie bis zum gewünschten Datum zu füllen Einstellungen: MaxDate – Wähle das gewünschte

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.





1. Any indicator such as MA and Volumes
2. Any chart except Renko and Candlesticks
3. News and Fundamentals