234 skills found · Page 1 of 8
stevejgordon / CorrelationIdAn ASP.NET Core middleware component which synchronises a correlation ID for cross API request logging.
SJTU-DMTai / MASTERThis is the official code and supplementary materials for our AAAI-2024 paper: MASTER: Market-Guided Stock Transformer for Stock Price Forecasting. MASTER is a stock transformer for stock price forecasting, which models the momentary and cross-time stock correlation and guide feature selection with market information.
seanwood / Gcc NmfReal-time GCC-NMF Blind Speech Separation and Enhancement
Shrediquette / PIVlabParticle Image Velocimetry for Matlab, official repository
eqcorrscan / EQcorrscanEarthquake detection and analysis in Python.
ColinLii / CC FJpyA Python Package for seismic ambient noise cross-correlation and the frequency-Bessel transform method
Lu-Feng / CricaVPROfficial repository for the CVPR 2024 paper "CricaVPR: Cross-image Correlation-aware Representation Learning for Visual Place Recognition".
WenkeHuang / FCCLTPAMI2023 & CVPR2022 - Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity Learning & Learn From Others and Be Yourself in Heterogeneous Federated Learning
xiahouzuoxin / Fftfft and cross-correlation
EZ4BYG / Signal ToolsSignal Processing toolbox, including DFT, IDFT, Wavelet, τp transform, HHT. Besides, this repository aslo has other useful functions, such as 1D/2D Convolution, Cross-Correlation, Filtering and Denosing.
alopatindev / Sync Audio TracksAudio tracks synchronization command-line tool for video editors that don't support it
lermert / NoisiA tool for modeling and source inversion of auto- and cross-correlations of ambient seismic "noise".
xlwangDev / HC Net[NeurIPS 2023] Fine-Grained Cross-View Geo-Localization Using a Correlation-Aware Homography Estimator
pearsonkyle / Signal AlignmentAlgorithms to align 1D signals via cross correlation and likelihood maximization.
juryxy / JuSpaceThis is the repository for the JuSpace toolbox allowing for cross-modal correlations between imaging data and positron emission tomography derived information
bideeen / Building A Trading Strategy With Pythontrading strategy is a fixed plan to go long or short in markets, there are two common trading strategies: the momentum strategy and the reversion strategy. Firstly, the momentum strategy is also called divergence or trend trading. When you follow this strategy, you do so because you believe the movement of a quantity will continue in its current direction. Stated differently, you believe that stocks have momentum or upward or downward trends, that you can detect and exploit. Some examples of this strategy are the moving average crossover, the dual moving average crossover, and turtle trading: The moving average crossover is when the price of an asset moves from one side of a moving average to the other. This crossover represents a change in momentum and can be used as a point of making the decision to enter or exit the market. You’ll see an example of this strategy, which is the “hello world” of quantitative trading later on in this tutorial. The dual moving average crossover occurs when a short-term average crosses a long-term average. This signal is used to identify that momentum is shifting in the direction of the short-term average. A buy signal is generated when the short-term average crosses the long-term average and rises above it, while a sell signal is triggered by a short-term average crossing long-term average and falling below it. Turtle trading is a popular trend following strategy that was initially taught by Richard Dennis. The basic strategy is to buy futures on a 20-day high and sell on a 20-day low. Secondly, the reversion strategy, which is also known as convergence or cycle trading. This strategy departs from the belief that the movement of a quantity will eventually reverse. This might seem a little bit abstract, but will not be so anymore when you take the example. Take a look at the mean reversion strategy, where you actually believe that stocks return to their mean and that you can exploit when it deviates from that mean. That already sounds a whole lot more practical, right? Another example of this strategy, besides the mean reversion strategy, is the pairs trading mean-reversion, which is similar to the mean reversion strategy. Whereas the mean reversion strategy basically stated that stocks return to their mean, the pairs trading strategy extends this and states that if two stocks can be identified that have a relatively high correlation, the change in the difference in price between the two stocks can be used to signal trading events if one of the two moves out of correlation with the other. That means that if the correlation between two stocks has decreased, the stock with the higher price can be considered to be in a short position. It should be sold because the higher-priced stock will return to the mean. The lower-priced stock, on the other hand, will be in a long position because the price will rise as the correlation will return to normal. Besides these two most frequent strategies, there are also other ones that you might come across once in a while, such as the forecasting strategy, which attempts to predict the direction or value of a stock, in this case, in subsequent future time periods based on certain historical factors. There’s also the High-Frequency Trading (HFT) strategy, which exploits the sub-millisecond market microstructure. That’s all music for the future for now; Let’s focus on developing your first trading strategy for now! A Simple Trading Strategy As you read above, you’ll start with the “hello world” of quantitative trading: the moving average crossover. The strategy that you’ll be developing is simple: you create two separate Simple Moving Averages (SMA) of a time series with differing lookback periods, let’s say, 40 days and 100 days. If the short moving average exceeds the long moving average then you go long, if the long moving average exceeds the short moving average then you exit. Remember that when you go long, you think that the stock price will go up and will sell at a higher price in the future (= buy signal); When you go short, you sell your stock, expecting that you can buy it back at a lower price and realize a profit (= sell signal). This simple strategy might seem quite complex when you’re just starting out, but let’s take this step by step: First define your two different lookback periods: a short window and a long window. You set up two variables and assign one integer per variable. Make sure that the integer that you assign to the short window is shorter than the integer that you assign to the long window variable! Next, make an empty signals DataFrame, but do make sure to copy the index of your aapl data so that you can start calculating the daily buy or sell signal for your aapl data. Create a column in your empty signals DataFrame that is named signal and initialize it by setting the value for all rows in this column to 0.0. After the preparatory work, it’s time to create the set of short and long simple moving averages over the respective long and short time windows. Make use of the rolling() function to start your rolling window calculations: within the function, specify the window and the min_period, and set the center argument. In practice, this will result in a rolling() function to which you have passed either short_window or long_window, 1 as the minimum number of observations in the window that are required to have a value, and False, so that the labels are not set at the center of the window. Next, don’t forget to also chain the mean() function so that you calculate the rolling mean. After you have calculated the mean average of the short and long windows, you should create a signal when the short moving average crosses the long moving average, but only for the period greater than the shortest moving average window. In Python, this will result in a condition: signals['short_mavg'][short_window:] > signals['long_mavg'][short_window:]. Note that you add the [short_window:] to comply with the condition “only for the period greater than the shortest moving average window”. When the condition is true, the initialized value 0.0 in the signal column will be overwritten with 1.0. A “signal” is created! If the condition is false, the original value of 0.0 will be kept and no signal is generated. You use the NumPy where() function to set up this condition. Much the same like you read just now, the variable to which you assign this result is signals['signal'][short_window], because you only want to create signals for the period greater than the shortest moving average window! Lastly, you take the difference of the signals in order to generate actual trading orders. In other words, in this column of your signals DataFrame, you’ll be able to distinguish between long and short positions, whether you’re buying or selling stock.
JuliaSeismo / SeisNoise.jlAmbient Noise Cross-Correlation in Julia
sunrise666 / NCCStereo Matching -- Normalized Cross Correlation by python
evanmayer / EffexSoftware-defined radio complex correlator. FX architecture: Polyphase filterbank Fourier transform spectrometer and cross-correlation, powered by GPU-accelerated, concurrent processing.
zhangprofessor / Fast Non Local Means And Asymptotic Non Local MeansNon-Local means denoising (NLM) algorithm is a milestone algorithm in the field of image processing. The proposal of NLM has opened up the non-local method which has a deep influence. This paper performed a revisit for NLM from two aspects as follows: 1. To alleviate the high computational complexity problem of NLM, a fast algorithm was constructed, which was based on cross-correlation and fast Fourier transform; 2. NLM always blur structures and textures during the noise removal, especially in the case of strong noise. To solve this problem, an Asymptotic Non-Local Means image denoising algorithm is put forward, which uses the property of noise variance to control the filtering parameters. Numerical experiments illustrate that the fast algorithm is 27 times faster than classical implementation with standard parameter configuration, and the ANLM uniformly outperforms classical NLM, in terms of both PSNR and visual effects.
