賭Nio一年半內執笠 [2]
clkong
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姐係今個星期五有機跌50蚊？
係呀 聽日準備all in put
覺得係回彈之後先橫行 一個月後先折爆
點都會彈吓嘅
已訓身 準備收錢
升到285再開put 都未遲
幾時會有大公司出嚟告鳩crwd 
我隻gm咁都跌到屎咁 T車畫大餅仲可以反彈
想請教一下樓主點睇台積電同天然氣未來
希望今晚可以跌30% 
Crwd咁快跌完
點解唔再跌多啲
天然氣輸到甩褲
樓主有無update
應否溝貨
應否溝貨屌佢老母
入返通知聲
aig終於可以食 咁就2倍
岩岩搵到隻教育股 cour 樓主點睇
燈到咁 介唔介意賣下SPY咁？樓主可能潛左水了
打算坐嘅真係要諗諗 呢隻野歷史高位4千幾 會decay的
俄羅斯想 Trump 贏
中國想 Harris 贏
中國想 Harris 贏
強帖留名
失蹤咗嘅呢段時間
每日擺幾個鐘落去砌個 System
搞多兩個禮拜可以搵個真 Account 試
已經搞好咗 Charles Schwab 個 Trader API
個系統會連去我個 Brokerage Account 自己 Trade
用到嘅 Technic 有:
Advanced Machine Learning - Ensemble Learning Models, Time-Series Analysis, Reinforcement Learning
Real-Time Market Analysis - News Sentiment Analysis with NLP, Technical Indicator Integration
Automated Trading System, Dynamic Risk Management
Continuous Model Refinement - Backtesting Framework, Version Control in Models
Languages: Python, Java
Library: Pandas, NumPy, Scikit-Learn, TensorFlow/PyTorch, NLTK/Spacy
最新嘅 Model (Version 7.4)
Backtesting & Simulation trades Result: (2.5 years)
Approximate profit 11x
USD 15,000 initial investment would have grown to around USD 165,000 during this period.
Version History:
Version 1.0:
First Complete Model: Combined the stop-loss, target price, and holding period strategies into a cohesive trading model.
Version 1.1:
Improved Stop-Loss: Adjusted stop-loss triggers based on volatility and average trading volumes.
Version 1.2:
Partial Sell Strategy: Added a mechanism to partially sell holdings at different target price levels.
Version 1.5:
Market Trend Adjustment: Introduced basic adjustments to the model based on overall market trends (bullish/bearish).
Version 2.0:
Financial Data Integration: First integration of fundamental financial metrics, such as revenue and profit margins, into the scoring model.
Version 2.3:
Advanced Stop-Loss Logic: Improved stop-loss logic by incorporating volatility-based adjustments.
Version 2.6:
Improved Target Price Logic: Introduced dynamic target prices based on insider confidence levels and company performance.
Version 3.0:
Advanced Backtesting: Implemented a more sophisticated backtesting system to simulate trades over longer periods with more accuracy.
Version 3.4:
Insider Weighting: Adjusted scoring to give more weight to insider trades from highly ranked executives or board members.
Version 4.0:
Sentiment Analysis: Integrated basic news sentiment analysis into the model to adjust scores based on news data.
Version 4.5:
Enhanced Sentiment Weighting: Improved the weighting of news sentiment based on the historical accuracy of publishers and authors.
Version 5.0:
Real-Time Monitoring: Added real-time data processing capabilities to make intraday adjustments to strategies.
Version 5.1:
Technical Indicators: Introduced basic technical indicators, such as moving averages, into the decision-making process.
Version 5.2:
Sentiment-Driven Adjustments: Implemented dynamic adjustments to stop losses and holding periods based on real-time sentiment data.
Version 5.3:
Market Condition Adjustments: Enhanced the model to be more responsive to changing market conditions, with a focus on minimizing risk in downturns.
Version 6.0:
RSI Integration: Added Relative Strength Index (RSI) as a key technical indicator for identifying overbought/oversold conditions.
Version 6.2:
Dynamic Scoring Updates: Improved the model's ability to update scores dynamically as new data (news, financials, RSI) comes in.
Version 6.4:
Profit Maximization: Refined the strategy to better capture gains in trending markets while managing risks through enhanced stop-loss mechanisms.
Version 7.0:
Comprehensive Financial Analysis: Further integrated detailed financial data (from the symbol_financials table) for a more robust evaluation of companies.
Version 7.1:
Advanced Sentiment Analysis: Improved sentiment analysis by considering the historical performance of news publishers and authors.
Version 7.2:
Enhanced Risk Management: Refined risk management strategies by incorporating sentiment-driven stop losses and holding periods.
Version 7.3:
Technical Analysis Suite: Introduced a full suite of technical indicators, including RSI, to refine buy/sell decisions.
Version 7.4:
Real-Time Market Condition Response: Final refinement focusing on real-time market data and sentiment to optimize profit while balancing risk effectively.
每日擺幾個鐘落去砌個 System
搞多兩個禮拜可以搵個真 Account 試
已經搞好咗 Charles Schwab 個 Trader API
個系統會連去我個 Brokerage Account 自己 Trade
用到嘅 Technic 有:
Advanced Machine Learning - Ensemble Learning Models, Time-Series Analysis, Reinforcement Learning
Real-Time Market Analysis - News Sentiment Analysis with NLP, Technical Indicator Integration
Automated Trading System, Dynamic Risk Management
Continuous Model Refinement - Backtesting Framework, Version Control in Models
Languages: Python, Java
Library: Pandas, NumPy, Scikit-Learn, TensorFlow/PyTorch, NLTK/Spacy
最新嘅 Model (Version 7.4)
Backtesting & Simulation trades Result: (2.5 years)
Approximate profit 11x
USD 15,000 initial investment would have grown to around USD 165,000 during this period.
Version History:
Version 1.0:
First Complete Model: Combined the stop-loss, target price, and holding period strategies into a cohesive trading model.
Version 1.1:
Improved Stop-Loss: Adjusted stop-loss triggers based on volatility and average trading volumes.
Version 1.2:
Partial Sell Strategy: Added a mechanism to partially sell holdings at different target price levels.
Version 1.5:
Market Trend Adjustment: Introduced basic adjustments to the model based on overall market trends (bullish/bearish).
Version 2.0:
Financial Data Integration: First integration of fundamental financial metrics, such as revenue and profit margins, into the scoring model.
Version 2.3:
Advanced Stop-Loss Logic: Improved stop-loss logic by incorporating volatility-based adjustments.
Version 2.6:
Improved Target Price Logic: Introduced dynamic target prices based on insider confidence levels and company performance.
Version 3.0:
Advanced Backtesting: Implemented a more sophisticated backtesting system to simulate trades over longer periods with more accuracy.
Version 3.4:
Insider Weighting: Adjusted scoring to give more weight to insider trades from highly ranked executives or board members.
Version 4.0:
Sentiment Analysis: Integrated basic news sentiment analysis into the model to adjust scores based on news data.
Version 4.5:
Enhanced Sentiment Weighting: Improved the weighting of news sentiment based on the historical accuracy of publishers and authors.
Version 5.0:
Real-Time Monitoring: Added real-time data processing capabilities to make intraday adjustments to strategies.
Version 5.1:
Technical Indicators: Introduced basic technical indicators, such as moving averages, into the decision-making process.
Version 5.2:
Sentiment-Driven Adjustments: Implemented dynamic adjustments to stop losses and holding periods based on real-time sentiment data.
Version 5.3:
Market Condition Adjustments: Enhanced the model to be more responsive to changing market conditions, with a focus on minimizing risk in downturns.
Version 6.0:
RSI Integration: Added Relative Strength Index (RSI) as a key technical indicator for identifying overbought/oversold conditions.
Version 6.2:
Dynamic Scoring Updates: Improved the model's ability to update scores dynamically as new data (news, financials, RSI) comes in.
Version 6.4:
Profit Maximization: Refined the strategy to better capture gains in trending markets while managing risks through enhanced stop-loss mechanisms.
Version 7.0:
Comprehensive Financial Analysis: Further integrated detailed financial data (from the symbol_financials table) for a more robust evaluation of companies.
Version 7.1:
Advanced Sentiment Analysis: Improved sentiment analysis by considering the historical performance of news publishers and authors.
Version 7.2:
Enhanced Risk Management: Refined risk management strategies by incorporating sentiment-driven stop losses and holding periods.
Version 7.3:
Technical Analysis Suite: Introduced a full suite of technical indicators, including RSI, to refine buy/sell decisions.
Version 7.4:
Real-Time Market Condition Response: Final refinement focusing on real-time market data and sentiment to optimize profit while balancing risk effectively.