AI-Driven Automated Trading Systems

Automatically creating recurring transactions to improve accuracy and efficiency in creating future transactions in a sequence of recurring payments. The system uses machine learning to predict if a sequence will continue and when the next transaction will occur based on historical data. When a new transaction matches the sequence characteristics, it checks if it falls within the predicted time window. If so, it recognizes it as the next recurring transaction and uses that to create future recurring transactions for that user-payee-amount combination. This accounts for variations in recurrence intervals.

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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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Evaluating transaction requests received by a computer system using a machine learning algorithm to improve accuracy in granting versus declining transactions. The computer system trains a machine learning model using historical transaction requests. When a new request comes in, the model scores it and compares to a threshold. If above, the request is granted, if below, declined. This improves accuracy compared to just using a fixed threshold as the model learns from prior requests. However, wrongly rejecting or granting transactions can degrade performance. To mitigate this, the model is retrained periodically using a subset of recent requests to adapt to changing conditions. This allows updating the model without having to retrain from scratch.

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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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Using machine learning to improve efficiency of asset-exchange platforms like loan marketplaces by predicting optimal listing parameters and prices based on historical data and attributes. The system trains machine learning models to determine importance scores for attributes like loan size, interest rate, etc. It then uses these scores to predict listing prices and categories for new assets. This feedback is provided back to users to optimize future listings and avoid delays.

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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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Automatically identifying counterpart entities from transaction strings in financial accounts when the counterpart entity is not explicitly listed. The method involves using machine learning models to translate the transaction strings into identified counterpart entities. The models are trained on manually mapped transactions and then retrained based on their predictions to improve accuracy. This allows automating the identification of counterpart entities from transaction strings that don't explicitly list them.

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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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Automated transaction initiation and processing for devices in a network like IoT without requiring user input. The system uses machine learning to analyze transaction history and device attributes to intelligently initiate and execute transactions autonomously. It creates a knowledge base of transactions for devices in the network, detects patterns, and creates triggers to initiate transactions based on device needs without user involvement.

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