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TradingPopup Multi-Source Fusion Deep Learning Predictive Model Solution (CNN-LSTM & TimeGPT & DQN) March 11, 2025 admin Model Products

This solution leverages the TradingPopup trading terminal/GPU workstation/GPU hyper-converged appliance/VGPU desktop to integrate text-based news, market data, and economic indicators from multiple sources—such as Bloomberg, Reuters, and user databases. By incorporating state-of-the-art deep and reinforcement learning algorithms (CNN-LSTM, DQN, TimeGPT, etc.), it delivers multi-perspective, multi-timeframe financial asset price forecasting, trading strategy generation, and order strategy formulation.

I. Core Models and Applicable Scenarios

1. CNN-LSTM: Multi-Timeframe Fusion for Mid- to Low-Frequency Trading

  • Applicable Timeframes: Daily / 4H / 1H / 15M mid- to low-frequency trading scenarios, ideal for fund managers, quantitative private equity, and banks with moderate trading frequency demands.

  • Model Principles:

    • CNN (Convolutional Neural Network): Extracts local patterns from multiple data channels such as news text and technical indicators.

    • LSTM (Long Short-Term Memory): Identifies both long-term and short-term dependencies in time series data, effectively capturing turning points and trend shifts.

  • Key Advantages:

    • Multi-Source Data Fusion: Incorporates textual sentiment and macroeconomic indicators to strengthen the understanding of key market drivers.

    • Robust Trading Signals: Reduces noise interference and improves reliability in buy/sell point detection.

    • Risk Management: Estimates ranges of volatility and potential risk points, assisting in position sizing and setting stop-loss/profit targets.

2. DQN: Ultra-High-Frequency Trading Model from Seconds to Minutes

  • Applicable Timeframes: From 1-minute or shorter intervals down to “seconds-level” trading scenarios, ideal for quantitative high-frequency funds, market makers, and teams sensitive to extremely fast data rates.

  • Model Principles:

    • Reinforcement Learning Q-Value Iteration: Evaluates the value of each action-environment interaction to form dynamically optimal trading decisions in high-frequency settings.

    • Adaptive Correction: Rapidly responds to subtle order book changes and unexpected news events, continuously refining the strategy.

  • Key Advantages:

    • Capturing Extremely Short-Window Opportunities: Quickly filters out optimal buy/sell decisions under rapidly shifting market conditions.

    • Deep Integration with Order Flow/Order Book: Allows fine-grained modeling of order queues and matching mechanisms, boosting real-time responsiveness.

    • High Extensibility: Integrates with multi-factor analysis and order book intent detection for advanced high-frequency strategies.

3. TimeGPT: Transformer-Based Complex Time Series Forecasting

  • Applicable Timeframes: From daily bars to longer cycles, or scenarios requiring forecasting of multi-level, multi-asset macroeconomic interplay and intricate time series.

  • Model Principles:

    • Utilizes the Transformer architecture to deeply model time-series data, featuring long-range dependency capture and multi-head attention for richer time-series representations.

    • Processes large volumes of features (e.g., cross-market prices, corporate events, macroeconomic indicators) in a multi-layered fashion, uncovering latent market patterns to deliver deeper strategic insights.

  • Key Advantages:

    • Complex Trend Analysis: Suited for handling macro policy shocks, multi-asset interactions, and other more complex market fluctuations.

    • Short- & Long-Term Coverage: Leverages attention mechanisms to focus on both recent volatility and global long-range structural patterns in the data.

    • Flexible Integration: Can complement or compare with CNN-LSTM, and can also combine with traditional statistical models (ARIMA-GARCH, etc.) for multi-model fusion.

II. Data Ingestion and Processing Workflow

1. Data Sources and Preprocessing

  • Text News: Extract sentiment, emotion pulses, or topic factors using NLP models like RoBERTa and T5.

  • Market Prices and Economic Indicators: Gather historical time-series data of various assets along with macro/micro indicators (e.g., Geco Indicator), aligning, denoising, and normalizing them as needed.

2. Feature Engineering

  • Construct multidimensional time-series features, including standard OHLC bars, trading volumes, technical indicators (MACD, RSI, etc.), along with factors extracted from text.

  • Integrate features required by CNN-LSTM, TimeGPT, and DQN, building specialized input datasets for mid-/low-frequency vs. ultra-high-frequency trading tasks.

3. Model Training and Inference

  • CNN-LSTM: Captures trend and cyclical patterns in mid-/low-frequency time series to forecast future price trajectories and volatility over multiple steps.

  • DQN: Iterates rapidly in second- or sub-minute-level markets, forming Q-functions for high-frequency trades.

  • TimeGPT (Optional): A Transformer-based time-series prediction engine, either as a complement or contrast to CNN-LSTM.

4. Results Storage and Visualization

  • Derived Bucket: Centralizes generated forecasts, trading signals, and risk control metrics.

  • Timestream Database: Enables real-time data queries and historical backtracking.

  • Web Chart Front End: Provides visualized charts and real-time dashboards, supporting multidimensional result comparisons and factor analysis.

III. Broad Model Coverage for Various Scenarios

  • CNN-LSTM: Focuses on trend analysis and robustness, ideal for mid-range trading or portfolio construction.

  • TimeGPT: Emphasizes complex market trend analysis, well-suited for advanced trading strategies across multiple assets.

  • DQN: Targets short-term decision-making and high-frequency returns, helping capture ultra-fast market “windows” in seconds-level trading.

  • Supports multi-model weighting or selection to suit diverse investment styles and strategic requirements.

IV. Multi-Source Data Fusion

  • Simultaneously handles news text, market data, and macroeconomic indicators, preventing bias from relying on a single data source.

  • Improves awareness and predictions for breaking news, policy fluctuations, and other external factors, delivering quicker risk alerts to users.

V. Deep Learning vs. Traditional Statistical Models

  • Offers ARIMA-GARCH, LSTM, FinBert, FinRL, and other models for reliable prior benchmarks.

  • Facilitates multi-model comparisons and fusion, assisting in strategy evaluation and optimal model selection.

VI. End-to-End Automated Workflow

  • Local Deployment: Users can realize data ingestion, preprocessing, model training, inference, and visualization on the TradingPopup terminal/GPU workstation/GPU hyper-converged appliance/VGPU desktop—fully contained in a closed-loop workflow.

  • Cloud Deployment: An official cloud computing environment (GPU cluster) is also available to reduce local hardware maintenance and smoothly scale resources, meeting elastic computing needs.

VII. Typical Application Scenarios

  • Quantitative Hedge Funds: Combine CNN-LSTM and DQN in a multi-strategy framework, triggering trading signals at different frequencies to enhance robustness and profitability.

  • High-Frequency Trading Funds: The DQN model specializes in second-level order-flow decisions, quickly exploiting tiny spreads and improving liquidity and profit margins.

  • Investment Research / Risk Control Departments: Employ TimeGPT’s in-depth insights to proactively detect risks and macro-level fluctuations, performing stress tests in scenarios involving multi-asset interaction or complex strategies.

  • Individual Professional Traders: Harness TimeGPT for mid-/long-term market trends and integrate DQN or CNN-LSTM for short-term volatility, better managing trading risks and capital efficiency.