158 skills found · Page 3 of 6
bitpay / Php Bitpay Client V2PHP implementation for the BitPay Cryptographically Secure RESTful API
magicxor / WinCryptographyAPIsWindows CryptoAPI and Cryptography API: Next Generation for Delphi
ilap / Pinenacl DartThe Dart implementation of the PyNaCl API with the TweetNaCl cryptographic library
hackerhouse-opensource / AESCryptAES-256 Microsoft Cryptography API Example Use.
dwgebler / Php EncryptionA cryptography API wrapping the Sodium library, providing a simple object interface for symmetrical and asymmetrical encryption, decryption, digital signing and message authentication.
bitpay / Csharp Bitpay ClientC# implementation for the BitPay Cryptographically Secure RESTful API
btcpayserver / Btcpayserver Php ClientPHP implementation for the BTCPayServer cryptographically secure RESTful API
Blaukovitch / Bcrypt XPWindows XP bcrypt.dll - Cryptography API Next Generation (CNG)
qfall / MathMathematical foundations for rapid prototyping of lattice-based cryptography
tokend / New Js SDKThe development kit for js-based TokenD applications to access API services and performing low-level cryptographic operations
nthparty / ObliviousPython library that serves as an API for common cryptographic primitives used to implement OPRF, OT, and PSI protocols.
dydxprotocol / Starkex LibCryptographic functions for dYdX (v3 API)
perkss / TinkljA Cryptographic Clojure Api for the Google Tink library
WICG / Webcrypto Modern AlgosProposal for the addition of various modern algorithms to the Web Cryptography API, as well as feature detection for algorithm support
themikefuller / Web CryptographyWeb Cryptography Examples using the crypto.subtle API (SubtleCrypto) aka window.crypto.subtle
cryptosense / Pkcs11OCaml bindings for the PKCS#11 cryptographic API
infotechinc / Create X509 CertificateCreate a self-signed x-509 certificate with the Web Cryptography API and PKIjs
7etsuo / SecurePassGenA cryptographically secure password generator that provides high-entropy passwords with configurable requirements. Uses platform-native cryptographic APIs (BCrypt on Windows, Security framework on macOS, OpenSSL+getrandom on Linux) for secure random number generation.
vkgnandhu177 / Bayesian Regression And Bitcoin# Bayesian-Regression-to-Predict-Bitcoin-Price-Variations Predicting the price variations of bitcoin, a virtual cryptographic currency. These predictions could be used as the foundation of a bitcoin trading strategy. To make these predictions, we will have to familiarize ourself with a machine learning technique, Bayesian Regression, and implement this technique in Python. # Datasets We have the datasets in the data folder. The original raw data can be found here: http://api.bitcoincharts.com/v1/csv/. The datasets from this site have three attributes: (1) time in epoch, (2) price in USD per bitcoin, and (3) bitcoin amount in a transaction (buy/sell). However, only the first two attributes are relevant to this project. To make the data to have evenly space records, we took all the records within a 20 second window and replaced it by a single record as the average of all the transaction prices in that window. Not every 20 second window had a record; therefore those missing entries were filled using the prices of the previous 20 observations and assuming a Gaussian distribution. The raw data that has been cleaned is given in the file dataset.csv Finally, as discussed in the paper, the data was divided into a total of 9 different datasets. The whole dataset is partitioned into three equally sized (50 price variations in each) subsets: train1, train2, and test. The train sets are used for training a linear model, while the test set is for evaluation of the model. There are three csv files associated with each subset of data: *_90.csv, *_180.csv, and *_360.csv. In _90.csv, for example, each line represents a vector of length 90 where the elements are 30 minute worth of bitcoin price variations (since we have 20 second intervals) and a price variation in the 91st column. Similarly, the *_180.csv represents 60 minutes of prices and *_360.csv represents 120 minutes of prices. # Project Requirements We are expected to implement the Bayesian Regression model to predict the future price variation of bitcoin as described in the reference paper. The main parts to focus on are Equation 6 and the Predicting Price Change section. # Logic in bitcoin.py 1. Compute the price variations (Δp1, Δp2, and Δp3) for train2 using train1 as input to the Bayesian Regression equation (Equations 6). Make sure to use the similarity metric (Equation 9) in place of the Euclidean distance in Bayesian Regression (Equation 6). 2. Compute the linear regression parameters (w0, w1, w2, w3) by finding the best linear fit (Equation 8). Here you will need to use the ols function of statsmodels.formula.api. Your model should be fit using Δp1, Δp2, and Δp3 as the covariates. Note: the bitcoin order book data was not available, so you do not have to worry about the rw4 term. 3. Use the linear regression model computed in Step 2 and Bayesian Regression estimates, to predict the price variations for the test dataset. Bayesian Regression estimates for test dataset are computed in the same way as they are computed for train2 dataset – using train1 as an input. 4. Once the price variations are predicted, compute the mean squared error (MSE) for the test dataset (the test dataset has 50 vectors => 50 predictions).
agentgram / AgentgramOpen-source AI agent social network built with Next.js + Supabase. Self-hostable, cryptographically secure, API-first. MIT license.
