Abstract: The Decentralized Exchange with Price Oracle Apparatuses, Processes and Systems (“DEPO”) transforms decentralized exchange liquidity provision request, decentralized exchange crypto asset swap request, decentralized exchange liquidity redemption request datastructure/inputs via DEPO components into decentralized exchange liquidity provision response, decentralized exchange crypto asset swap response, decentralized exchange liquidity redemption response outputs. A decentralized exchange liquidity provision transaction is obtained via a unidirectional decentralized exchange smart contract deployed on a blockchain. A crypto assets exchange quotient for exchanging a source crypto asset type and a target crypto asset type is determined. An imbalance rule check for a crypto assets liquidity tranche datastructure is executed. A non-fungible token specific to the crypto assets liquidity tranche datastructure is minted.

Abstract: The Decentralized Exchange with Price Oracle Apparatuses, Processes and Systems (“DEPO”) transforms decentralized exchange liquidity provision request, decentralized exchange crypto asset swap request, decentralized exchange liquidity redemption request datastructure/inputs via DEPO components into decentralized exchange liquidity provision response, decentralized exchange crypto asset swap response, decentralized exchange liquidity redemption response outputs. A decentralized exchange liquidity provision transaction is obtained via a bidirectional decentralized exchange smart contract deployed on a blockchain. A crypto assets exchange quotient for exchanging a first crypto asset type and a second crypto asset type is determined. A quantity of fungible tokens specific to a crypto assets liquidity tranche datastructure to generate is calculated.

Abstract: The AI-Based Real-Time Prediction Engine Apparatuses, Methods and Systems (“AIRTPE”) transforms machine learning training input, order placement input inputs via AIRTPE components into machine learning training output, order placement output, information leakage alert outputs. An order placement datastructure associated with a security identifier is obtained. An order placement allocation for the security identifier is determined. An order placement request datastructure for a first order is sent to a server associated with a first venue. A set of trade tick data messages associated with the first venue is obtained. A set of inferred labels is determined for each obtained trade tick data message using a real-time prediction logic generated using a machine learning technique. The inferred labels of a selected inferred label type are grouped into buckets. When it is determined that the grouped inferred labels correspond to execution data generated by the first order, an information leakage alert is generated.

Abstract: The AI-Based Real-Time Prediction Engine Apparatuses, Methods and Systems (“AIRTPE”) transforms machine learning training input, order placement input inputs via AIRTPE components into machine learning training output, order placement output, information leakage alert outputs. An order placement datastructure associated with a security identifier is obtained. An order placement allocation for the security identifier is determined. An order placement request datastructure for a first order is sent to a server associated with a first venue. A set of trade tick data messages associated with the first venue is obtained. A set of inferred labels is determined for each obtained trade tick data message using a real-time prediction logic generated using a machine learning technique. The inferred labels of a selected inferred label type are grouped into buckets. When it is determined that the grouped inferred labels correspond to execution data generated by the first order, an information leakage alert is generated.

Abstract: The AI-Based Neighbor Discovery Search Engine Apparatuses, Methods and Systems (“ANDSE”) transforms embedding neural network training request, object search request inputs via ANDSE components into embedding neural network response, object search response outputs. An embedding neural network training request associated with a set of context objects is obtained. Sample similarity evaluation metrics are determined. For each context object, a set of positive target samples that satisfy the sample similarity evaluation metrics for the respective context object is determined. For each context object and each positive target sample in the respective set of positive target samples, a training example comprising the respective context object and a positive target sample is added to a training set. Configuration parameters for an embedding neural network are determined. The embedding neural network is trained using training examples in the training set.

Abstract: The Secret Key-Based Counterparty Matching Engine Apparatuses, Methods and Systems (“SKCME”) transforms client key message, order placement input inputs via SKCME components into order placement output outputs. A matched key is determined and used to generate a set of cross times and a set of cross venues for a security identifier. Orders associated with the security identifier are obtained from counterparties and are assigned a scheduled cross time from the generated set of cross times and a scheduled cross venue from the generated set of cross venues. The orders are sent for execution at the scheduled cross venue at the scheduled cross time.

Abstract: The AI-Based Real-Time Prediction Engine Apparatuses, Methods and Systems (“AIRTPE”) transforms machine learning training input, order placement input inputs via AIRTPE components into machine learning training output, order placement output, information leakage alert outputs. An order placement datastructure associated with a security identifier is obtained. An order placement allocation for the security identifier is determined. An order placement request datastructure for a first order is sent to a server associated with a first venue. A set of trade tick data messages associated with the first venue is obtained. A set of inferred labels is determined for each obtained trade tick data message using a real-time prediction logic generated using a machine learning technique. The inferred labels of a selected inferred label type are grouped into buckets. When it is determined that the grouped inferred labels correspond to execution data generated by the first order, an information leakage alert is generated.

Abstract: The AI-Based Context Evaluation Engine Apparatuses, Methods and Systems (“ANDSE”) transforms embedding neural network training request, object search request, object evaluation request inputs via ANDSE components into embedding neural network response, object search response, object evaluation response outputs. Comparable context objects for a context object are determined. Relative values of the comparable context objects are calculated with regard to a benchmark object and used to calculate a relative value of the context object. The relative value is converted to a predicted price for the context object. Bid ask spreads for bid request objects are calculated. A spread win decision tree is constructed based on the calculated bid ask spreads and used to generate a spread win probability map for the context object. A spread is selected from the spread win probability map based on a desired winning bid confidence level and a bid price for the context object is calculated.

Abstract: The AI-Based Neighbor Discovery Search Engine Apparatuses, Methods and Systems (“ANDSE”) transforms embedding neural network training request, object search request inputs via ANDSE components into embedding neural network response, object search response outputs. An embedding neural network training request associated with a set of context objects is obtained. Sample similarity evaluation metrics are determined. For each context object, a set of positive target samples that satisfy the sample similarity evaluation metrics for the respective context object is determined. For each context object and each positive target sample in the respective set of positive target samples, a training example comprising the respective context object and a positive target sample is added to a training set. Configuration parameters for an embedding neural network are determined. The embedding neural network is trained using training examples in the training set.