AI Trading Models for Financial Markets

An artificial intelligence system for analyzing financial data and making investment recommendations using a decentralized ecosystem of agents like models, recommenders, and verifiers. The agents compete to provide the best financial models for analyzing data. Verifiers evaluate the models and recommenders select them. The agents are incentivized to perform well through a competition where winners collect stakes from losers. This creates a stable equilibrium where agents strive to provide accurate models. The ecosystem also uses costly signaling with cryptographic tokens to distribute rewards and rents.

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Method for converting order book data into 2D format suitable for machine learning models. The method involves generating a tensor data structure with a time axis and price axis in tick units. The tensor has multiple 2D data items representing quantities for bid and ask prices at specific coordinates defined by time and price. A mid price calculated from the bid and ask is used as the tick price. This converts the tabular order book data into a format that can be directly input into 2D CNN models for stock price prediction.

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Using neural networks to make financial investment predictions and recommendations based on user data, news, financial data, and predictions. The neural networks are trained to determine investment movements, prices, and recommendations by processing this input data. An interactive system like a dialogue agent can use the neural networks to provide financial advice when users ask questions about investments.

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Using neural networks to make financial investment predictions and recommendations. The neural networks analyze data like user information, news, financial data, and predictions to determine financial movements, stock prices, investment suggestions, etc. This allows more accurate and personalized investment advice compared to relying solely on external sources. The system can also be interactive, like a chatbot, to provide customized financial insights and advice to users.

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Trade execution engine that helps users evaluate different market conditions and make final trading decisions by using a switching matrix model to reconcile multiple trading strategy algorithms. The engine receives user trade instructions and market conditions, generates matrices representing market states and execution strategies, defines signals based on predicted market states, selects the appropriate matrix node for the predicted state, and executes the associated strategy. It can also switch between execution phases with different strategies based on updated inputs.

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Analyzing cryptocurrency-related information using artificial intelligence to provide personalized trading recommendations. The system uses real-time credibility analysis and AI prediction to analyze cryptocurrency-related info from multiple sources. It determines the credibility of the info based on historical congruence with events. AI predicts market trends using the credibility-weighted info. A processor generates personalized trading decisions based on the predicted trends.

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Automatically predicting transaction likelihood and timing using machine learning to improve order management. The technique involves training machine learning models on historical transaction data to predict confidence levels, conversion probabilities, and expected timelines for pending transactions. This enables automated actions like optimizing resource allocation and forecasting based on more accurate and timely insights into deal progress.

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A method for improving reinforcement learning accuracy in applications like portfolio optimization by augmenting the state space with additional information. The augmented state includes predictions from machine learning models trained on external data sources like news articles and financial data. This allows the reinforcement learning agent to leverage diverse and heterogeneous information beyond just the raw input state. The augmented state is then used to train the reinforcement learning model for tasks like portfolio optimization. The augmented state helps improve performance compared to using just the original input state.

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High frequency trading system that uses a combination of accelerator and FPGA for machine learning models along with a reconfigurable processor for pre/post processing. The system receives market data from servers, generates prediction reference data, feeds it to a dedicated accelerator for machine learning inference, then sends the predictions to the FPGA-based reconfigurable processor for order generation. This allows leveraging specialized hardware for ML while keeping flexible pre/post processing on the FPGA.

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Method for high frequency trading that uses machine learning to predict stock prices and generate orders for maximum profit. The method involves calculating latency for market orders based on batch sizes and selecting an optimal batch size to balance speed and accuracy. It also considers input data precision when calculating latency. By optimizing batch size and precision, it aims to generate orders with valid predicted prices at the right time points for maximum profit.

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AI-powered order prioritization for fulfillment that accurately ranks orders for an organization based on factors beyond just dollar value and service level agreements. The method involves training a ranking-based machine learning model using historical order data to predict order prioritization. The model considers features like product category, customer history, urgency, profit margin, etc. to predict relevance scores for each order. Orders are then ranked by score for fulfillment.

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Generating feature embeddings for financial transaction data that can be used in machine learning models to improve prediction accuracy and interpretability. The embeddings are generated in an automatic and unsupervised manner using a heterogeneous graph representation of the transactions. The graph connects users and merchants based on transaction pairs with specific characteristics. The embeddings are generated hierarchically by jumping between transaction pairs based on frequency and hyperparameters. This allows extracting features with context and relationships between transactions. The embeddings can be labeled, indexed, and consumed by models without extra training.

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Training an AI model to predict the minimum winning price in real-time bidding without splitting auction types. The model is trained using auction data with conditional minimum winning price probability distributions. The overall minimum winning price probability distribution is generated first, then conditional distributions for each auction history. The AI is trained using auction attributes as input and the conditional distributions as output. This allows accurate price prediction in both auction types without dividing them.

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A system for facilitating electronic trading of financial instruments that uses machine learning to match traders and determine trade likelihood. The system allows users to initiate electronic trading sessions for specific financial instruments with specific terms. It matches the initiating user with dealer users who have a history of trading similar instruments. It also ranks potential invitee users based on likelihood of trading the instrument. This matching and ranking is done using machine learning models on historical trading data. The system then coordinates the trading session with selective sharing of offers.

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Method and system for high frequency stock trading that leverages machine learning models while avoiding issues like time gaps and latency. The method involves generating input data based on market data, feeding it to a machine learning model to predict stock prices at future times, and generating orders based on those predictions. To prevent time gaps, the input batch size is chosen based on latency to select a future time point before the batch arrives. This allows using the model without outdated predictions.

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A system for efficiently processing, organizing, and searching financial market data using vector representations. The system involves pre-processing the raw market data into vectors that capture the underlying structure and patterns. This involves filtering and transforming the data to extract meaningful features. The vectors can then be used for machine learning and analysis tasks. The search function allows finding similar historical market periods based on vector comparisons, enabling efficient exploration of related data.

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Selecting the optimal machine learning model for time series financial data with variable label distributions by training multiple models using oversampled training data with varying label distributions. During training, the label distribution is changed by oversampling specific labels. This generates a set of models with different label distribution biases. When making a prediction on new time series data, the similarity of its label distribution to the training distributions is measured and the most similar model is selected. This allows adaptive model selection based on the input data's label distribution.

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High frequency trading system that uses stored machine learning models to enable fast and low latency processing for high frequency trading applications. The system generates executable files of machine learning models by interpreting the model code rather than compiling it. These executable files are then stored in local memory. This allows the models to be executed directly from memory without needing to load and interpret the model code every time. This provides much faster inference times compared to compiling and loading the model code for each trade.

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Simulation environment for training reinforcement learning agents in a dynamic resource environment like a trading platform. The simulation involves a matching engine that receives tasks from agents requiring resources and matches them based on compatibility. Unmatched tasks are sent back to agents. Agents compete for resources non-deterministically. Historical tasks are also provided for agents to generate variants. The matching engine provides training data by communicating executed tasks and resource impacts to agents.

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An automated trading platform that uses self-updating trading bots to find and deploy the optimal trading strategy over time. The platform generates a set of trading bots with parameters defining execution rules. It searches through the bots using historical data to find an initial ideal bot that tracks asset performance. This bot is deployed for a duration, evaluated, and then the search is repeated based on the initial bot's success. The platform deploys a new ideal bot for another duration. This iterative process allows the platform to dynamically adapt and improve its trading strategies over time using machine learning techniques.

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Automated order management technique for order problem learning and mitigation using reinforcement learning. The technique analyzes historical orders to identify issues and failures. It then generates a state-based data structure that reflects successes and failures of actions associated with previous orders. This structure is used to identify issues in pending orders and recommend mitigating actions. It also calculates rewards based on order completion to train a reinforcement learning model that learns remediation steps for failures.

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Reinforcement learning-based asset management system that leverages machine learning and optimization techniques to help investors make better portfolio decisions. The system takes input from investors like stock picks and account balances, learns their preferences, and provides optimized portfolio adjustments. It also enables retirement planning with AI-driven withdrawal optimization across multiple accounts. The system uses reinforcement learning algorithms like T-REX and G-Learner to learn investors' goals and preferences, and then provides personalized portfolio recommendations.

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Mass execution of analytical models to predict optimal decisions in real-time for transaction negotiations. The system iteratively verifies data sources, generates transaction options, scores them using models, verifies compliance, and optimizes based on preferences. It determines a minimally viable transaction structure to constrain variation range. This parallel, asynchronous, non-blocking execution scales models across dimensions to quickly find optimal transactions.

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Detecting abnormal trading activity using machine learning models that learn normal trading patterns for market participants. The models are trained on historical participant and external factor data using recurrent neural networks. They proactively monitor current trading patterns to identify deviations that pose risk, beyond normal thresholds given current conditions. The models use both participant and external data to identify abnormalities in real time.

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Automated stock prediction using machine learning techniques like random forests to accurately forecast stock prices with reduced risk. The method involves collecting stock data, identifying stocks of interest, defining time ranges, applying random forest decision trees to predict stock movements, presenting results, and providing alerts for trading. The random forest model uses supervised and unsupervised learning to train prediction and estimation models. It can leverage indicators like RSI and MACD.

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Automating pricing desk operations in financial services using machine learning to optimize offer decisions. The system takes a proposed offer with parameters and context, trains a machine learning model on historical offers and decisions, and iteratively generates and scores modified offers to find an optimized one with high acceptance probability.

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Forecasting financial time series like stock prices using deep learning that accurately models both internal patterns and external factors affecting the data. The technique involves splitting the historical data into sequences representing internal patterns and external factors. A machine learning model with a neural network architecture is built using these sequences. It combines a non-linear deep learning model for internal patterns with a kernel function for external factors. Parameters are learned to equitably model both. The model generates forecasts by combining the sequences with the learned parameters. Parameters are adjusted based on comparison with actual values.

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Simplifying complex market investments using AI-assisted trading recommendations and dynamic risk assessment. The platform uses AI to generate simplified trading recipes for complex investment opportunities based on factors like market sentiment, user preferences, and historical data. It ingests disparate sources of financial and sentiment data, analyzes it with machine learning, and packages investment opportunities with preconfigured trading recipes that match user risk profiles. This allows non-professional traders to execute complex strategies like options without deep knowledge or research.

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An AI-based real-time prediction engine for trading applications that leverages machine learning to analyze market data and trading activity to provide insights and recommendations. The engine takes inputs like training data, order requests, and trade prints, and outputs predictions, alerts, and adjusted orders. The engine uses advanced feature engineering to extract meaningful signals from market and trading data, and employs AI techniques to learn patterns and relationships. This allows the engine to provide insights into market participants, surveillance capabilities, and adaptive order entry parameters based on counterparty analysis.

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Explainable artificial intelligence (AI) system for financial decision making that provides transparent and auditable explanations for AI-based financial transaction decisions. The system uses a neural network model for decision making, but also has an explainability module that analyzes the neural network weights to determine feature importance and node impact. This allows tracing back through the network to explain how each input feature contributes to the final decision. The explainability module can also identify biases and imbalances in the data. The explained decisions are presented to users, along with options to override the AI if desired. The system also allows defining functional guidelines to mitigate errors.

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A dynamic conditional alpha estimation and financial intelligence platform automation system for more accurate risky asset return prediction. The system uses a recursive multivariate filter to extract dynamic conditional factor premiums. It also has a conditional specification test to distinguish static and dynamic multifactor asset pricing models. The dynamic alphas correlate better with reward-to-risk ratios than static alphas. The platform automation involves an interactive social network for users to share financial intelligence.

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Execution of stock orders using reinforcement learning to optimize trading strategies for individual stocks. The system collects historical stock data, trains a model using reinforcement learning to derive order execution strategies, and then executes orders in real time using the learned strategies. The reinforcement learning involves a model with multiple neural networks that determines action policies and estimates values for a reinforcement agent. The model is trained using stock data to derive strategies that maximize future rewards like trade execution cost. This allows the system to dynamically adapt and respond to market conditions for each stock when executing orders.

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Stock trading system that uses reinforcement learning to optimize stock order execution strategies. The system collects historical trading data for a stock, generates subsidiary predictions using a supervised learning model, and derives an order execution strategy using a reinforcement learning model. This strategy is then applied in real-time to execute orders. The reinforcement learning model maximizes future compensation by learning from trading rewards.

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Training a reinforcement learning agent for automated trading using historical feature data to normalize current feature inputs. The agent generates resource request signals based on its output. At each time step, it receives a current feature set for a resource, maintains historical feature sets, computes normalized features using historical stats, appends them to the current state, and feeds the supplemented state to train the agent. This improves training stability by accounting for changing input distributions.

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Generating and synchronizing trading signals from financial market data at low latency and high throughput for use by latency-sensitive trading applications. The technique involves leveraging parallel computing resources to compute trading signals from market data streams faster than traditional software execution. The signals are then delivered synchronously with the market data to trading applications. This allows latency-sensitive trading strategies using estimators and other signals that can be computed and delivered at the same speed as market data. The signals are appended to market data messages or delivered separately with a synchronization identifier to match the timings.

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Cost-efficient reinforcement learning framework for optimal trade execution in dynamic markets that reduces training overhead while improving performance. The framework combines deep reinforcement learning and planning to increase sample efficiency. It uses a learnable environment model to approximate market impact from real experiences. This enhances policy learning via the learned environment. A state-balanced exploration scheme solves bias caused by non-increasing inventory during trade execution. The framework interleaves policy updates from real and simulated experiences. By learning from both environments, it increases sample efficiency and outperforms direct RL.

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Machine learning approach to optimize order routing in high frequency trading to improve execution outcomes by dynamically modifying orders using a stepwise optimization algorithm. The approach involves maintaining a virtual order configuration separate from the actual executed orders. The virtual configuration is iteratively modified using a stepwise optimization technique that removes and adds orders to converge on an optimal configuration. The virtual configuration is then sent en masse to the execution venues to replace the actual orders. This allows converging on an optimal configuration without sending frequent order updates mid-optimization.

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Detecting grey market orders and taking actions against them in real-time during the ordering process. The method involves using a machine learning model trained on historical order data to detect grey market orders. The model scores new orders based on their similarity to clusters of historical orders. If the score indicates a high likelihood of a grey market order, actions like cancelling, price adjustment, or quantity limits can be taken before fulfillment. The model is trained with historical data and user feedback to refine its accuracy.

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High-frequency trading system that uses neural networks to infer the best execution algorithm for a given set of security values based on a fit of those values into predetermined functions associated with different algorithms. The system uses dedicated buffers to load executable code and parameters for the algorithms. This allows parallelized computing with multiple processors to reduce latency and enhance performance. The neural networks are trained to fit historical security data into the predetermined functions to determine the best algorithm for current values. This provides customized, optimized execution based on the specific characteristics of the security.

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An intelligent and adaptive real time valuation engine using neural networks and regression analysis to provide accurate sub-millisecond valuations of products in highly fragmented, global, interlinked markets. The engine leverages LSTM neural networks to identify long term dependencies between data characteristics and transaction values. It receives input data from multiple sources, including continuous streaming channels, and assigns dynamic weighting to each predicted value based on the input data's influence. This adaptive weighting improves valuation accuracy in complex, interconnected markets with high volume and variety of data.

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Training ensembles of prediction models for optimal investment performance in different market regimes. The method involves expanding the training dataset to prevent overfitting. It creates an ensemble of models, each optimized for a specific trading pattern, to predict optimal outcomes for new datasets. This allows leveraging the strengths of multiple models for each pattern instead of trying to fit a single model to all patterns.

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Proactively predicting large orders and providing fulfillment support using machine learning techniques. The system classifies quotes as large orders based on size parameters and applies machine learning to determine supportability of converting those quotes into orders. It then outputs fulfillment information to entities involved in order fulfillment based on the determined supportability. This allows proactive prediction and support for large orders instead of reactively handling them.

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Artificial intelligence-based investment platform that uses machine learning algorithms to generate personalized portfolio recommendations, mitigate risk, diversify investments, and provide social networking features. The platform creates user profiles with risk tolerance, return goals, and investment accounts. It analyzes securities using distinct AI modules for factors like value, technicals, sentiment. A weighted aggregation of recommendations is generated based on historical correlation. The platform autonomously buys/sells securities and shows progress toward goals. It also provides holistic net worth views across accounts and social networking.

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A method to provide recommended asset portfolio compositions for future time periods when historical financial data is not available. The method involves solving a quadratic unconstrained binary optimization (QUBO) problem using a quantum computer to find optimal trading trajectories for a known past period. A machine learning algorithm is trained using the past optimal trajectories and historical financial data. For a future period without full historical data, the trained algorithm is run with the available financial data to provide a recommended portfolio composition.

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Training a generative neural network to model market uncertainty and use it for portfolio optimization. The network learns the probability distribution of asset prices over time given past prices. This allows generating realistic future price trends. The network is trained on historical data. The probability distribution learnt by the network is then used to find diversified portfolios with optimal risk-return tradeoffs by simulating multiple market scenarios.

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Intelligently automating financial trading using both structured and unstructured data to improve trading performance compared to traditional algorithmic trading. The system combines structured time-series financial data with unstructured text data like news articles to analyze sentiment and make trading decisions. It uses a neural network to predict future prices based on current data, sentiment, and historical trends. This allows the system to provide trading recommendations that consider factors like market conditions and news events in addition to price history. The aim is to provide more intelligent and flexible automated trading that can adapt to changing market conditions and provide better returns compared to simple algorithmic trading.

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Using a neural network to predict stock price movements after earnings calls by leveraging insights from the call transcripts, stock price history, and sector data. The neural network takes features extracted from the earnings call transcripts, historical stock prices, and industry sector data as input to predict the stock price movement after the earnings call. The features are generated using techniques like word embedding, recurrent neural networks, and attention mechanisms.

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Configurable virtual robots for automated trading that can be customized and deployed by users without coding knowledge. The robots are created by selecting modules and functions in a graphical interface. Users can also choose and lease pre-made robots. The robots can analyze markets, manage portfolios, and execute trades. The system retrieves blockchain data to authorize users for leased robots. It allows users to create, share, and lease robots in a marketplace.

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Detecting abnormalities like crashes in financial markets using recurrent neural networks trained on flow data representing stock prices. The method involves processing stock price data over time to generate flow feature data representing velocity and density. This flow feature data is applied to a recurrent neural network to assess if it indicates an abnormality in the future. A cost-sensitive recurrent neural network is used to address class imbalance. The flow representation improves detection accuracy. The neural network combines flow features with macroscopic variables.

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Autonomous trading using neural networks with memory to learn optimal stock trading decisions. The method involves training a memory-enabled neural network on stock price time series to predict future prices. This network's learned parameters are used to initialize a reinforcement learning neural network. The first network's output is augmented into the second network's state for continuous trading decisions at each time point.

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