Rajesh Kumar Nait / プロファイル
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4 年
経験
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34
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41
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0
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現在の役割では、デイトレーダーとして毎日$SOLを取引し、私のセラーページで先端プログラムを販売しています。これらのプログラムには、WebSocketとAPIを介してすべての人気取引所からチャートをロードするネイティブWebSocket暗号通貨取引所接続ユーティリティが含まれています。VPSでシームレスに動作し、外部DLLは必要ありません。
暗号通貨のチャート分析や取引ツールに興味がある場合は、DMで気軽に連絡してください。私のMQL5セラーページで暗号製品や取引所の統合をチェックしてください。ISTタイムゾーンで1日12時間のサポートを提供しています。
期間限定で、すべての製品が30%割引で提供されています。お見逃しなく!
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コネクター for MT5 – MT5とのシームレスな統合 概要 Pionex API EAコネクター for MT5 は、 MetaTrader 5 (MT5) と Pionex API をスムーズに統合するツールです。トレーダーは MT5 から直接、取引の実行・管理、残高情報の取得、注文履歴の追跡ができます。 主な機能 🔹 アカウントと残高管理 Get_Balance(); – Pionex の現在のアカウント残高を取得。 🔹 注文の実行と管理 orderLimit(string symbol, string side, double size, double price); – 指定価格で 指値注文 を実行。 orderMarket(string symbol, string side, double size, double amount); – 指定量で 成行注文 を実行。 Cancel_Order(string symbol, string orderId); – ID による特定の注文のキャンセル。
Pionex Live MT5 Data and History -- APIアドレスを ツール > エキスパートアドバイザー に追加 api.pionex.com ws.pionex.com 手順: シンボルの作成 CreateSymbols = true を選択 MT5ターミナルを再起動 ( 重要! ) マーケットウォッチでシンボルを選択 し、履歴とリアルタイムデータをロード モード: LiveUpdate – チャートにユーティリティを追加し、取引データを取得 History – 過去の履歴データを指定した日付と時間まで埋める 設定: MaxDate – 更新したい履歴の日付を選択 ALL – 指定した日付から履歴を埋める AutoUpdate = true – MT5を再起動するたびに履歴を自動更新
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.

