61 skills found · Page 1 of 3
questdb / QuestdbQuestDB is a high performance, open-source, time-series database
TalEliyahu / Awesome Business Of CybersecurityExplore cybersecurity’s market dynamics through public companies, capital flows, acquisitions, and product segments.
cemsinano / PykapKAP (Public Disclosure Platform) Documentation Wrapper for Capital Markets Board of Turkey and Borsa Istanbul Public Disclosures.
emceeKim / Korea Finance MCPKorea's first legally-safe (Capital Markets Act compliant) finance MCP. 15 tools (ECOS · Real Estate · DART · KRX) + 2 Korea-unique synergy tools.
open-source-modelling / Insurance PythonAll Python algorithms published by Open Source Modelling in one place.
compatibl / TradeentryTrade entry from natural language for the capital markets using LLMs
bottama / Stochastic Asset Pricing In Continuous TimePredicting stock prices using Geometric Brownian Motion and the Monte Carlo method
mariourquia / Cre Skills Plugin100+ institutional-grade CRE skills for Claude Code, Desktop, and Cowork. Deal screening, underwriting, structuring, capital markets, asset management, leasing, investor relations, development, disposition, and daily ops. 50+ expert agents, 12+ Python calculators, 10+ orchestrator pipelines, catalog-driven routing.
nacho-herrera / PyCocosPython library to connect with Cocos Capital Rest APIs. Market Data and Order Routing are supported.
olekssy / EvkitA library for SEC data extraction, equity valuation, discovery of mispriced stocks
Cryptoaj-hack / DFDTOKENDecentralized Finance (DeFi) Development Services & Solutions Eliminate the role of a middleman by availing decentralized finance (DEFI) development services & solutions. Get access to the major financial services through a blockchain network and experience the benefits of automation, a higher level of security, anonymity, interoperability, and transparency. Our wide range of services include Market-Making Consulting We take immense efforts in establishing financial markets that understand the customers’ proprietary algorithms. We aim at improving the access of liquidity to investors and democratize the whole system. We render customized features according to the customer’s expected return on investment. Decentralized Crypto Banking We ensure a frictionless user experience by facilitating the direct transfer of value between the involved parties supported by decentralization. Our ready-to-launch white-label mobile payment apps render a variety of services such as wallet integration, value holding, and detailed transactional analysis. Defi Lottery System Development We provide a no-loss lottery system that benefits our participants completely. We take steps to eliminate the custodianship of the pooled capital. We permit investing your capital in other related dapps and distribute the rewards in form of a major share of the interest earned to a winner randomly selected by the smart contracts. We assure the regular flow of returns. Derivatives Over Defi Platform We ensure seamless access to derivatives and maximize your earning potential by many notches. by establishing robust dapps, we enable traders to hedge their portfolio of investments and minimize risks by directly engaging with their peers through a democratic platform. We are experts in derivatives market-making and Dapp platform development. Decentralized Fund Management All your crypto assets will be managed to yield high performance in a decentralized exchange through smart control and management. with in-depth experience in investment exchanges along with our strong knowledge of defi, we render our services at low fees and avoid potential risks. Defi Insurance System Development We ensure that there are no risks present in our smart contract. With our robust provision of insurance services, we assure you that there will be no chance of uncontrollable liquidity requests. We contain futuristic risks, uncertainties, and emergencies through lucrative insurance deals. Defi Yield Farming Platform Development Yield farming refers to the technique through which one can earn more cryptocurrencies by using his existing holding of cryptos. Liquidity providers play a vital role in the success of yield farming. They stake their assets in liquidity pools and facilitate trading in cryptos by creating a market. Defi Staking Platform Development Defi staking involves a mechanism where crypto assets will be staked on a supported wallet or exchange and passive income will be earned. The rewards can be calculated based on the quantity of staked assets, the staking duration, inflation rate, and the network issuance rate. Defi Lending Platform Development Defi lending platforms have been made popular by the likes of aave and compound. The basic features of a defi lending platform include flash loan facilities, a fiat payment gateway, and an exclusive margin trading facility, the advantages of defi lending include high immutability, better transparency, quick access, and resistance to transaction censorship. Defi Smart Contract Development One of the pivotal reasons behind the tremendous growth of defi services is due to the heavy investments made in robust defi smart contract development. They are created with the solidity programming language, highly encrypted, and automates the tasks to be executed based on certain pre-set terms and conditions. Defi Dapp Development Defi Dapp development plays a critical role to avoid the risk of a central point of failure. They are highly secure when compared to centralized applications due to the absence of a central authority. Defi Tokens Development Defi tokens development has played a critical role in boosting the growth of decentralized applications. Their value is currently higher than bitcoin. it has a huge trading volume and has garnered a lot of attention from the mainstream crowd in recent times. Defi Dex Development Like Uniswap Uniswap is one of the leading defi projects being undertaken. It is an innovative venture as it utilizes incentivized liquidity pools instead of regular order books. every user of uni swap will is rewarded with a percentage of fees incurred on every ethereum transaction for rendering liquidity to the system. Defi Wallet Development Traders will have complete control over their funds through defi wallet development without the interference of any authorities in the system. Supreme security is guaranteed for users without any compromise. By supplying customized private keys to every user, there will not be any chances for any loss of data. DeFi Marketing Services To assist DeFi projects gain user engagement, marketing services are indispensable.From drafting white paper, video and content marketing, to legal advisory, marketing and community management, our DeFi marketing and consulting services are well-versed to get the job done. DeFi Synthetic Asset Development Synthetic assets derive their value from underlying assets and derivatives which are essentially smart contracts. In DeFi, Synthetic assets have gained acclaim as they involve low risks and little chance of price fluctuations. Users can easily invest, trade, and own assets with no hassles. DeFi Solutions For Ecommerce Streamline your Ecommerce business with DeFi and its pragmatic tools. With DeFi’s solutions , benefits like omission of intermediaries, faster shipping, supply chain management, and real time tracking can be integrated with your Ecommerce business, increasing profits. DeFi Tokenization Development Tokenization Development is one of the pragmatic solutions DeFi offers. Users can now convert inoperative and underutilized assets into great profits by simply tokenizing their assets. With our DeFi tokenization, avail of ERC20, ERC721 & NFT tokens for your assets. DeFi Crowdfunding Platform Development Although a relatively new sector, DeFi crowdfunding has become the go-to mode of aggregating funds to support businesses and start-ups. Our DeFi Crowdfunding platform services come with additional benefits in the likes of tax benefits, instant approval, fundraising calendars and more. DeFi Real Estate Platform Development DeFi has revolutionized the ways of real estate management. Now real estate owners and investors, with the help of blockchain based tokens, can make property investment seamless and manageable. With fractional ownership, financial inclusivity is now possible. DeFi ICO Development One of the leading fundraising methods, DeFi ICO services are distinguished. Creating utile tokens, community management, escalating coin value, and launching projects with diligence & guidance from market analysts and blockchain experts is inclusive of our ICO Development. DeFi Exchange Development Offering users a plethora of apparent benefits, DEXs are the prized innovation of DeFi. Offering high-end security, durable liquidity, complete anonymity and financial inclusivity, DEXs make trading and transacting crypto accessible and lucrative for crypto enthusiasts. DeFi Protocol Like Yearn. Finance Yearn. Finance offers the best APY the market has to offer by referring to popular exchanges. This protocol offers its users the best yields in a highly secure network. With in-built smart contracts and an open source code, it supports a range of Stablecoins offering huge returns. DeFi Protocol Like AAve The DeFi protocol Aave offers crypto traders a robust platform for lending and borrowing of crypto for which they earn high interests. The highlight feature of Aave - Flash loans and flexible interest rates make it a profitable platform for crypto traders. DeFi Exchange Like 1inch 1inch exchange now has the reputation of being the DEX offering users the lowest slippage. As an aggregator, 1inch connects several exchanges to one platform in a non-custodial ecosystem. With governance and farming features, trading on 1inch remains prominent.
chrisgillam / Polymarket GambotGambot is an open-source trading bot that identifies profitable sports betting opportunities and executes them on Polymarket. By pulling market data from sharp sportsbooks (like Pinnacle) and comparing against Polymarket, Gambot leverages a probabilistic modeling approach and Kelly Criterion bet sizing to pursue long-term capital growth.
vshah5 / Equity Research Report And Stock PitchThis repository contains the equity research report (pdf), stock pitch slide deck (ppt), financial models (xl), and a couple other preliminary analysis slides all conducted for the purpose of researching Mead Johnson Nutrition company. All the documents were prepared by me for a potential investment opportunity when I was a Senior Analyst for the Student Investment Fund at the School of Accounting and Finance at the University of Waterloo.
eclipse-tradista / TradistaOpen Source Capital Markets Platform: Unified Cross-Asset Trading, Risk Management & Post-Trade Operations. Modular, Auditable, Sovereign.
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.
GAnilkumarreddy / Fibonacci Retracements ExtensionsThis python program is used to calculate fibonacci retracements and extensions using high and low value of a stock.Fibonacci tool is a popular technical tool for analysing stocks,currency,etc in capital markets.
theBigGavin / Marketingdashboard面向金融与产业研究的一屏式实时行情大屏：A股/港股/美股指数、大宗商品、美债收益率、板块热点、主力资金流、7×24 快讯、产业链自选股、AI 大模型 Token 追踪。A real-time market research cockpit on a single screen: CN/HK/US indices, commodities, treasury yields, sector hotspots, capital flows, 7×24 news, industry-chain watchlists and AI token usage trends.
SimonOuellette35 / MicrostructureCapital market microstructure data simulator/generator
assetCLI / AssetCLI InitassetCLI - Lovable for Internet Capital Markets. (Also, Goldman Sachs for Crypto X AI !!) Winner of the Solana MCP AI Agent Competition. Vibe coding bonding curves, DEX & DAOs
larsulbricht / Awesome Digital AssetsCollection of high-quality resources on blockchain, tokenization, and DLT-based capital markets (EU-Focus)
