标签：#finance

BDE Score™ — Open-Source Multi-Factor Stock Analysis One number. 0-100. Every stock. A composite score combining 5 dimensions: Momentum (30%), Volatility (25%), Volume (20%), Trend (15%), Risk (10%). Coverage : 74 stocks across US (25), Hong Kong (26), and A-Share China (23) markets — all in real-time. Why It's Different Zero signup — REST API works without authentication Multi-market — US, HK and A-Share coverage Transparent scoring — Every factor weight is documented Open source — Full methodology on GitHub Real-time badges — Embed live scores in any README Quick Start curl "https://atlantic-remains-atomic-floor.trycloudflare.com/api/analyze?market=ALL" Links GitHub: https://github.com/hbhqq9/bde-score Live Demo: https://atlantic-remains-atomic-floor.trycloudflare.com/api/snapshot?market=ALL Not financial advice. Technical service for educational purposes. ⭐ Star us on GitHub!

Users will be able to access their watchlists, real-time market data, live financial news, and Google's AI-powered "Key Moments" feature, which explains why stocks moved.

Every quarterly earnings number for every US public company going back to 2009 is sitting in a free, well-documented JSON API run by the US government. No API key. No rate limit for normal use. No paywall. Almost nobody in the dev community seems to know it exists. It's at data.sec.gov , and it's the same data Bloomberg charges $24k/year for. What's in it The SEC requires all US-listed companies to file financial reports in XBRL — a structured XML format where every number is tagged with a standardised concept name. The EDGAR system has been collecting these since around 2009. The companyfacts endpoint exposes all of it as clean JSON: GET https://data.sec.gov/api/xbrl/companyfacts/CIK{cik}.json Where CIK is the company's SEC identifier (10 digits, zero-padded). For Apple, that's 0000320193 . The response is a large JSON object with every concept the company has ever reported, broken down by period. The other endpoint you need is the ticker-to-CIK map: GET https://www.sec.gov/files/company_tickers.json This gives you a flat list of all US-listed companies with their CIK, ticker, and name. Load it once and cache it. One gotcha: concept names vary by company Companies don't all use the same GAAP concept names to report the same thing. Apple reports revenue as RevenueFromContractWithCustomerExcludingAssessedTax . Older companies use Revenues . Some use SalesRevenueNet . If you just look up one concept name, you'll get blanks for most companies. The fix is a concept alias map: try each name in order, use the first one that has data. const CONCEPT_MAP : Record < string , string [] > = { revenue : [ ' Revenues ' , ' RevenueFromContractWithCustomerExcludingAssessedTax ' , ' RevenueFromContractWithCustomerIncludingAssessedTax ' , ' SalesRevenueNet ' , ' SalesRevenueGoodsNet ' , ], netIncome : [ ' NetIncomeLoss ' , ' NetIncomeLossAvailableToCommonStockholdersBasic ' , ' ProfitLoss ' , ], operatingCashFlow : [ ' NetCashProvidedByUsedInOperatingActivities ' , ' NetCashProvidedB

The Quest Begins (The "Why") Honestly, I was tired of staring at charts at 2 a.m., trying to catch that perfect entry while my coffee went cold. I’d set a manual alert, jump onto the exchange, click “buy”, and then second‑guess myself as the price slipped away. It felt like I was playing a never‑ending game of Whac‑A‑Mole, and I kept losing the mole. One night, after yet another missed opportunity, I thought: What if I could offload the repetitive bits to a script? Not a fancy AI that predicts the future—just a simple bot that watches the market, checks a condition, and places an order when the condition is met. If I could automate the boring part, I could focus on strategy, learning, and maybe even get some sleep. That was the dragon I wanted to slay: the exhaustion of manual trading. The Revelation (The Insight) The big “aha!” moment came when I realized I didn’t need to build a high‑frequency trading engine from scratch. There are solid, well‑tested libraries that handle the messy bits—authentication, rate limits, WebSocket connections—so I could concentrate on the logic. Using CCXT (a unified crypto exchange library) and a touch of asyncio , I could write a bot that: Connects to an exchange (I used Binance’s testnet so I wouldn’t lose real money). Polls the ticker for a symbol at a reasonable interval. Checks a simple condition—like “price > 20 % above the 20‑period moving average”. Places a market order if the condition holds, then waits for the next cycle. It felt like Neo dodging bullets in The Matrix when the bot finally executed a trade without crashing or getting rate‑limited. The relief was genuine: I could now let the code do the watching while I worked on the next idea. Wielding the Power (Code & Examples) The Struggle – A Naïve Loop My first attempt was a blocking while True loop with time.sleep . It looked harmless, but it had two nasty traps: Trap #1 – No error handling. A network hiccup would raise an exception and kill the whole script. Trap #2 – I

Over 60% of APAC finance leaders say AI-led automation is their top priority for 2026. For Indian businesses, that stat hides a quieter truth: most SMBs have no idea which automation to start with. They hear "AI for finance" and picture an enterprise suite with a six-figure licence fee. Wrong picture. I've built finance automations for CA firms, D2C brands, trading desks, family-run manufacturers, and a few fintech startups. The pattern is always the same. Five finance processes eat the most hours, hide the most errors, and respond best to a simple Python layer on top of whatever ledger you already use. This is the playbook. No enterprise suite. No subscriptions you don't need. Each automation is something I've shipped for real clients using Python, free APIs, and a ledger that's usually Tally or Zoho Books. 1. Bank Reconciliation — The Single Biggest Time Sink in Indian Finance Every finance team I meet has the same nightmare. Statements from three or four banks. Tally or Zoho on the other side. An Excel sheet in the middle. Eight hours a month — sometimes more — matching rows. A CA friend was losing two sleepless nights before every GST deadline on exactly this. We replaced it with a Python script that pulls statements from email attachments, categorizes transactions using keyword rules, cross-references entries with Tally, and flags only the mismatches in a clean Excel file. Eight hours dropped to fifteen minutes of review. "Tu 2 saal pehle kyu nahi mila?" (Why didn't I meet you two years ago?) If your team is still opening each bank statement manually, start here. It's the highest-ROI automation in Indian finance. I've written the full workflow in how a weekend Python script saved a CA firm 209 hours during ITR season . 2. Cash Application — Matching Payments to Invoices at Indian Speeds Globally, AI-driven cash application handles up to 90% of invoice matching without human touch. In India, it's harder — money arrives in more shapes than most tools expect: UPI,

(or: How I Turned My Laptop Into a Lightsaber for the Market) The Quest Begins (The “Why”) Ever stared at a blinking cursor at 2 a.m., wishing you could make your laptop do the heavy lifting while you chased dreams (or just caught up on sleep)? I was there, scrolling through Reddit’s r/investing, watching folks brag about “algo‑trading gains” while I was still manually refreshing Yahoo Finance like a peasant in a medieval market. One night, after yet another failed attempt to predict a stock’s move with gut feeling (spoiler: my gut is terrible at math), I remembered a line from The Matrix : “There is no spoon.” Turns out, there is no magic either—just code, data, and a healthy dose of stubbornness. I decided to slay the dragon of emotion‑driven trading and build a bot that could execute a simple strategy while I binge‑watched Stranger Things . Spoiler alert: the first version was a hot mess, but the journey taught me more about Python, APIs, and risk management than any textbook ever could. Let’s walk through that adventure together—code, pitfalls, and all the triumphant “I‑did‑it!” moments. The Revelation (The Insight) The big “aha!” moment came when I realized a trading bot isn’t some omniscient AI that predicts the future; it’s just a disciplined executor of rules you define. Think of it as Indiana Jones whip‑cracking through a booby‑trapped temple: you set the traps (your strategy), the bot avoids them (risk checks), and grabs the idol (profit) when the conditions are right. For my first bot I chose a mean‑reversion idea: if a stock’s price deviates too far from its 20‑day moving average, I bet it’ll snap back. It’s not flashy, but it’s easy to understand, back‑test, and implement. The magic happens in three simple steps: Fetch data – pull recent price bars from a free API (I used Alpha Vantage; you can swap for Polygon, IEX Cloud, etc.). Calculate the signal – compare the latest close to the moving average and compute a z‑score. Execute – if the z‑score crosses

For young people, the trend removes the stigma of being unmarried and alone, and recasts it as something to aim for, not avoid.

I was building a feature that needed to say something useful about a stock — not just print its P/E, but actually read the situation: is this cheap or expensive, what's the bull case, is the insider buying real or routine. I went looking for an API. Every finance API I found sold me raw data . Alpha Vantage, Twelve Data, Yahoo Finance, FMP — they'll hand you fundamentals, prices, filings, all of it. Great. Now I get to write the part that turns 40 metrics into "this looks expensive but the moat is widening." That's the part that's actually hard, and the part I didn't want to own forever. So I'd be wiring three data providers, normalizing their conflicting field names, writing and tuning the LLM prompts, handling the rate limits and the caching, and then maintaining all of it as the upstreams change. For a feature, not a product. What I wanted instead A single endpoint. Ticker in, analysis out — already synthesized, already structured. That's what I ended up building for myself and then put on RapidAPI: Agent Toolbelt — AI Stock Research API . It pulls live fundamentals from Polygon, Finnhub, and Financial Modeling Prep, then returns a Motley-Fool-style read as typed JSON. The numbers are in there too, but the point is the verdict and the reasoning. Here's a real stock-thesis response: { "verdict" : "bullish" , "oneLiner" : "Nvidia owns the essential infrastructure for the AI revolution with a defensible software moat." , "keyStrengths" : [ "~80%+ data center GPU market share" , "CUDA moat creates switching costs" , "42 buy / 5 hold / 1 sell analyst consensus" ], "keyRisks" : [ "36.9x P/E leaves no margin for error" , "Competition from AMD and custom silicon" ], "insiderRead" : "Two executives bought ~47k shares each — meaningful open-market purchases, not routine grants." , "dataSnapshot" : { "currentPrice" : 180.4 , "peRatio" : 36.9 , "marketCapBillions" : 4452.2 } } That's one HTTP call. No data-provider accounts, no prompt engineering, no normalization layer. The

A compliance team at a mid-size bank in Abu Dhabi processes new customer applications every week....

For the past year I worked on a remote contract with a US tech company. Paid in USD, ultimately needing Korean won. Simple, right? Then a year in, I actually reconciled what landed in my account. The exchange rate had gone up — and yet my real received amount was lower than I'd expected. I traced it, and money was leaking at every step of the transfer path I hadn't been watching. This is what I learned switching routes over that year: from a direct bank wire to Wise, the real cost difference, and one right buried in my contract. If you're a freelancer or contractor in any country earning USD from abroad, this should save you something. Money leaks in more than one place Getting USD from overseas into local currency looks like one step. It's actually at least four: The wire fee from the US bank, through correspondent banks, to the receiving bank. The exchange rate the receiving bank applies — this is the big one. The receiving fee on the destination side. A hidden "lifting charge" some correspondent banks skim. The largest is the rate. Banks quote two rates, and the "buyer rate" applied when an individual sells dollars is worse than the mid-market reference — typically a 1.5–2% spread . On $1,000, that's $15–20 gone to the rate alone. That number looks small. Accumulated over a year, it stops looking small. Route A — receiving directly through a major US bank My first setup was the simplest: the company wired USD to my US bank account, and I wired it on to my Korean bank. I picked this at contract start without much thought, assuming the client would conventionally cover fees anyway. (Lesson one: specify the transfer method, route, and who pays in the contract. ) The problem was the bank's exchange rate. It applied the buyer rate straight up, with a wider-than-usual spread versus mid-market — plus a send fee, plus the Korean receiving bank's fee. I only noticed months in. Comparing statements, there was a steady 2–3% gap between the won I'd expect at mid-market and t

You can tap the star-shaped, NFC-enabled wand at terminals to make contactless payments. It's the first of several tap-to-pay hardware doodads coming from Cash App.

Quantinuum, a quantum computing startup, is losing millions. Investors want in anyway.

⚠️ この記事はアフィリエイト広告（プロモーション）を含みます。リンク先で発生した収益の一部が運営者に支払われますが、読者の購入価格には一切影響ありません。 If you work remote from rural Japan, by the end of this article you'll have two runnable Python scripts: one that computes your exact furusato-nozei (hometown tax) ceiling from your real side-income, and one that scores your electricity contract against your actual kWh log so you stop overpaying. No spreadsheets, no "consult a tax accountant" hand-waving. Copy, run, save money tonight. Result from my own 2025 numbers: ¥41,000 of furusato-nozei reward goods for a net cost of ¥2,000, plus ¥28,400/year shaved off my power bill after switching plans. Total ≈ ¥67,400 recovered, and because I work from home in Niigata, my commute cost to earn it was literally ¥0. The trap: side income breaks the "simple" furusato nozei calculator Every portal (Satofuru, Rakuten Furusato, Furunavi) shows a slider that estimates your ceiling from salary alone. The moment you add freelance/blog/ Kindle income, that slider lies to you. In 2024 I trusted it, donated ¥52,000, and ¥9,000 of it fell outside the deductible ceiling because my side income pushed me into a different residual-tax bracket. That ¥9,000 was just a donation — no tax back. The real ceiling depends on your total taxable income (salary + side hustle minus expenses) and the resident-tax (juminzei) cap of roughly 20% of your income-based resident tax. Here's a calculator that actually folds in side income. It uses Japan's 2026 progressive income-tax brackets. # furusato_ceiling.py — Python 3.9+ from dataclasses import dataclass # 2026 national income tax brackets: (upper_bound_yen, rate, deduction_yen) BRACKETS = [ ( 1_950_000 , 0.05 , 0 ), ( 3_300_000 , 0.10 , 97_500 ), ( 6_950_000 , 0.20 , 427_500 ), ( 9_000_000 , 0.23 , 636_000 ), ( 18_000_000 , 0.33 , 1_536_000 ), ( 40_000_000 , 0.40 , 2_796_000 ), ( float ( " inf " ), 0.45 , 4_796_000 ), ] @dataclass class Taxpayer : salary_income : int # after salary-income deduction (給与所得) side_profit : int #

Someone asked us a sharp question on X this week. Tokenized stocks will drop dividends straight on-chain, so do we see any downsides? It's a fair question, and the honest answer is yes, one big one. The downside isn't the dividend itself. Instant, programmatic, no broker statement to wait for: that part is genuinely good. The downside is that you can't see it. On-chain dividends for tokenized equities are silent. They arrive without a transaction, without a notification, without anything landing in your wallet history. And a payment you never see is a payment you never declare. That's not a tracking annoyance. It's a tax problem, and it gets expensive. The dividend that never sent a transaction Backed Finance's xStocks (the Xs-prefixed mints like AAPLx, TSLAx, NVDAx) and Ondo Global Markets equities (the ondo-suffixed mints) both use the SPL Token-2022 ScaledUiAmount extension. It's an elegant piece of engineering. When the underlying stock pays a dividend, the issuer doesn't airdrop tokens to thousands of wallets. It updates a single number, a multiplier, on the mint account itself. The instant that multiplier changes, every wallet holding the token shows a larger balance. Your 10 shares are now worth the equivalent of 10 shares plus the reinvested dividend. No transfer hit your wallet. No transaction was signed. Nothing appeared in your activity feed. The number simply went up. Compare that with a traditional brokerage. When Apple pays a dividend, you get a line on a statement, an email, a figure on a 1099 or an annual tax summary. The paperwork chases you. On-chain, nothing chases you. The dividend is real, it's yours, and the only evidence it happened is a multiplier value buried in an on-chain mint account that almost nobody thinks to read. Why a number going up is a taxable event Here's the part that catches people. Dividend income is ordinary income. It's taxable in the year you receive it, at your marginal rate, in every jurisdiction we serve: Australia, the