DEV Community: Phi Thành

Real-Time Market Data Is the Missing Piece of AI Trading

Phi Thành — Sat, 13 Jun 2026 16:47:21 +0000

Artificial Intelligence has transformed nearly every industry. We now have AI agents that can write code, analyze documents, answer customer questions, and even generate software from a simple prompt.

Yet when it comes to trading, many AI systems still struggle with one fundamental problem:

They don't see the market in real time.

The Illusion of AI Trading

Many developers are building AI trading bots using large language models such as GPT, Claude, Gemini, or open-source alternatives.

The workflow often looks like this:

Fetch historical market data
Send data to an AI model
Ask for a trading decision
Execute the trade

On paper, it sounds impressive.

In reality, most AI trading systems are making decisions using delayed, incomplete, or outdated information.

Imagine asking a human trader to predict the next move in gold while only showing them yesterday's chart.

The result would be unreliable.

The same applies to AI.

Why Historical Data Is Not Enough

Historical data is essential for:

Backtesting
Training machine learning models
Identifying long-term patterns
Strategy development

But trading happens in the present.

Markets react instantly to:

Economic releases
Central bank announcements
Breaking news
Liquidity changes
Large institutional orders

An AI model that only sees historical candles is effectively driving while looking in the rearview mirror.

The Rise of AI Agents in Finance

A new generation of AI agents is emerging.

These agents can:

Analyze markets
Compare multiple assets
Generate trade ideas
Monitor risk
Execute actions automatically

However, an agent is only as good as the information it receives.

The smartest AI model in the world cannot make accurate trading decisions if its market data is delayed by minutes, hours, or even a single candle.

Data quality often matters more than model quality.

Real-Time Data Changes Everything

When an AI agent has access to real-time market data, it gains the ability to:

React Instead of Predict

Rather than guessing what happened, the AI can respond to what is happening now.

Monitor Multiple Markets Simultaneously

Humans can watch a handful of charts.

AI agents can monitor:

Gold
Forex
Stocks
ETFs
Indices
Cryptocurrencies

all at the same time.

Detect Opportunities Faster

Price movements, volatility spikes, and market anomalies can be identified within seconds.

Create Dynamic Strategies

Instead of relying on fixed rules, AI can continuously adapt to changing market conditions.

The Infrastructure Challenge

Many developers focus on models but overlook infrastructure.

Building an AI trading system requires:

Reliable market feeds
Fast APIs
Historical data
Streaming updates
Consistent symbol coverage
Low-latency access

Without these components, even the most advanced AI agent becomes limited.

This is why modern AI trading stacks increasingly depend on specialized market data providers rather than generic financial APIs.

The Future: AI + Real-Time Market Intelligence

The next wave of trading innovation will not be driven solely by larger models.

It will be driven by better context.

Just as web search transformed AI assistants by giving them access to current information, real-time market data will transform AI trading systems by giving them access to the current state of the market.

The winning AI traders of the future will combine:

Strong reasoning models
Real-time market data
Historical context
Risk management
Automated execution

Remove any one of these pieces, and performance suffers.

Final Thoughts

AI is no longer the bottleneck.

Models are becoming smarter every month.

The real challenge is providing those models with accurate, timely, and actionable market information.

For developers building the next generation of trading agents, the question is no longer:

"Which AI model should I use?"

The more important question is:

"How quickly and reliably can my AI understand what's happening in the market right now?"

Because in trading, intelligence without real-time data is just an educated guess.

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Introducing SaaSMon: A Better Way to Discover and Track SaaS Products

Phi Thành — Wed, 03 Jun 2026 15:52:07 +0000

The SaaS ecosystem has never been more active.

_Every day, founders launch new products. Developers build innovative tools. AI startups appear seemingly overnight. Yet despite the explosion of software products, discovering quality SaaS solutions remains surprisingly difficult.

Most directories are outdated. Product launch platforms focus on a single day of visibility. Search engines often favor companies with larger marketing budgets rather than the best products.

We built SaaSMon to solve that problem._

Why We Started SaaSMon

As founders ourselves, we constantly found ourselves searching for tools to help with development, marketing, analytics, automation, customer support, and dozens of other business needs.

The problem wasn't a lack of options.

The problem was finding the right options.

We wanted a place where people could discover SaaS products based on real categories, actual use cases, and current information—not just who had the biggest advertising budget.

At the same time, we saw many great founders struggling to get visibility for products they spent months or years building.

SaaSMon was created to bridge that gap.

What Is SaaSMon?

SaaSMon is a SaaS discovery platform designed to help users find software faster and help founders reach the right audience.

Our goal is simple:

Make SaaS discovery easier.
Give emerging products a chance to be found.
Create a growing catalog of useful software across every category.

Whether you're looking for an AI tool, a marketing platform, a developer utility, or a niche business solution, SaaSMon helps you explore products in one place.

Built for Founders

Launching a SaaS is hard.

Getting attention after launch is often even harder.

SaaSMon gives founders an additional channel to showcase their products, improve discoverability, and connect with potential users who are actively searching for solutions.

Instead of competing for a single launch day, products can continue to be discovered long after they're published.

Built for Users

For users, SaaSMon is about reducing the time spent searching.

Rather than jumping between search results, social media posts, and multiple directories, you can browse software by category, explore new products, and discover tools you may not have found otherwise.

Our focus is on making discovery simple, fast, and useful.

Our Vision

We believe the next generation of software discovery should be more transparent, more accessible, and more founder-friendly.

There are thousands of incredible SaaS products being built every year. Many never get the visibility they deserve.

Our mission is to help surface those products and make it easier for people to find software that genuinely solves their problems.

We're still early in the journey, and we're continuously improving SaaSMon based on feedback from founders and users.

If you've built something you're proud of, we'd love to see it listed.

And if you're searching for your next favorite SaaS tool, welcome to SaaSMon.

The discovery starts here.

You can check it by yourself: Here

How to Stream Live Forex Rates to Google Sheets API: A Complete Guide

Phi Thành — Sat, 23 May 2026 08:26:01 +0000

Table of Contents

Quick Answer: How to Stream Live Forex Rates to Google Sheets API
Why Streaming Live Forex Rates to Google Sheets API Is Harder Than It Looks
The Step-by-Step Approach
Tools for Streaming Live Forex to Google Sheets
Common Mistakes to Avoid When Using a Forex API in Google Sheets
How We Approach This Problem at RealMarketAPI
When to Start Using a Dedicated Forex API for Google Sheets
Frequently Asked Questions

Quick Answer: How to Stream Live Forex Rates to Google Sheets API

To stream live forex rates into Google Sheets, connect a dedicated market data provider through Google Apps Script, or bridge a WebSocket feed through a small server. That setup pulls fresh rates into your spreadsheet on a schedule you control, well past what the built-in functions can do.

Pick a provider with a genuine free tier and low latency. RealMarketAPI is one option: it serves live gold, forex, crypto, and stock prices over REST, WebSocket, or a Telegram Bot, and the free plan needs no credit card. You can have a key in under a minute.

With the right API behind it, a sheet can track currency pairs in near real time, feed a live dashboard, or fire an alert when a level breaks. The two things that decide whether it works are latency and uptime, and those are exactly where casual free feeds tend to fall down.

What Is the Fastest Way to Pull Live Forex Data Into Google Sheets?

Google Sheets ships with the GOOGLEFINANCE function, which covers some currency pairs out of the box. It refreshes roughly every 20 minutes, skips many pairs, and offers no historical depth.

The faster route is Google Apps Script calling a REST endpoint on a timer. The script fetches JSON, parses it, and writes the values straight into your cells. This is the pattern most developers settle on.

For true streaming, a WebSocket feed pushes ticks the instant they happen. Sheets cannot hold a socket open itself, so you route the stream through a small middle layer, which we cover below.

Why Streaming Live Forex Rates to Google Sheets API Is Harder Than It Looks

Dropping forex data into a spreadsheet sounds trivial. The friction shows up quickly.

Most free feeds lag by 15 seconds or more. Many cap you at a handful of calls per minute, and a 25-requests-per-day ceiling, which is what Alpha Vantage now enforces on its free tier, makes continuous streaming impossible.

Google Sheets adds its own ceilings. Custom functions and Apps Script triggers share quota, and a single script execution is capped at six minutes. Call an API too aggressively and the run dies mid-write, leaving you with half a sheet.

Why Does the GOOGLEFINANCE Function Fall Short for Real-Time Forex?

GOOGLEFINANCE is handy for stocks and the major pairs. It does not cover every instrument, and Google itself notes the data can be delayed by up to 20 minutes. The official GOOGLEFINANCE reference spells out the supported attributes.

It also returns a single price with no bid, no ask, no historical OHLC, and no indicators. For anyone tracking spreads or back-testing, that thin data model is a dead end.

What Latency Issues Do Free FX APIs Have?

Free APIs often resell data sourced from secondary providers. The fetch-then-cache cycle quietly adds seconds, sometimes minutes, of lag before a number reaches you.

Some also throttle hard during volatile sessions, exactly when you most want a current quote. The result is gaps in the sheet right when the market is moving.

The Step-by-Step Approach

A dependable link between a forex API and Google Sheets comes down to four steps that build on each other.

Step 1: Choose a Reliable Forex API for Google Sheets

Look for a real free tier, low latency, and broad pair coverage. Steer clear of anything that only updates every few minutes.

RealMarketAPI's free plan runs at sub-150ms latency and covers six core symbols (gold, silver, Bitcoin, Ethereum, EUR/USD, and Google) at 5,000 requests per month over REST. Paid plans extend that to 60+ instruments across forex, crypto, stocks, and commodities. The pricing page lists each tier, and the Free tier asks for no credit card.

Step 2: Connect Google Apps Script to the API

Apps Script is the bridge between your sheet and any REST endpoint. The official Apps Script documentation walks through the basics, and the minimal flow is short:

Open Extensions, then Apps Script, and create a new script.
Use UrlFetchApp.fetch() to call the API endpoint with your key.
Parse the JSON and write it with getRange().setValues().
Save the function so a trigger can run it.

Our API docs show the exact endpoint structure and response shape.

Step 3: Schedule Automatic Updates With Triggers

Time-driven triggers run your function on a schedule. A minutes timer gives you near real-time refreshes without touching the sheet by hand.

Keep the six-minute execution cap in mind. If you pull many pairs, split them across separate triggers or batch the requests so a single run stays well under the limit.

Step 4: Handle Errors and Rate Limits Gracefully

Every API enforces a request rate. Your script should read the HTTP status code and back off before retrying rather than hammering the endpoint.

Our API returns clear error codes alongside a Retry-After header, so a well-written script knows precisely how long to pause and recovers without leaving holes in the data.

Tools for Streaming Live Forex to Google Sheets

Several APIs can feed a spreadsheet. Here is an honest look at how three common choices compare.

Feature	Finnhub	Alpha Vantage	RealMarketAPI
Free plan	Yes	Yes	Yes (no credit card)
Free tier freshness	Real-time (US stocks)	Delayed 15-20 min	Sub-150ms (live)
WebSocket	Yes (stocks, currencies)	No	Yes (Plus plan and up)
Historical data	Plan dependent	20+ years (daily)	Up to 10 years (Business)
Technical indicators	No	50+ indicators	Indicators API (Pro and up)
Telegram Bot	No	No	Yes

Comparing Free Tiers for Forex API Google Sheets Use

Provider	Free tier request limit	Instruments on free tier	Free tier data freshness
Finnhub	60 calls per minute	Stocks, forex, crypto	Real-time (US stocks)
Alpha Vantage	25 requests per day	Stocks, forex, commodities	Delayed 15-20 min
RealMarketAPI	5,000 requests per month	6 core symbols	Sub-150ms (live)

All three offer a free tier, but they gate streaming differently. Finnhub bundles WebSocket into its free plan for stocks and currencies. Alpha Vantage offers no WebSocket at any tier. RealMarketAPI keeps its free tier REST-only with 5,000 requests per month, then unlocks WebSocket streaming on the Plus plan at $14.99 per month. If you want the full feature-by-feature breakdown, see our RealMarketAPI vs Finnhub and RealMarketAPI vs Alpha Vantage comparisons.

Common Mistakes to Avoid When Using a Forex API in Google Sheets

A few avoidable errors quietly cost time and accuracy.

What Happens When You Rely Solely on Free Data Sources?

Free APIs change their terms often. Coverage shrinks, limits tighten, and you usually find out only when a sheet stops updating.

Some free feeds also insert deliberate delays. That is fine for a hobby tracker and useless for anything time-sensitive, such as an automated signal.

How Does Polling Too Frequently Harm Your Sheet?

Calling the API every ten seconds feels productive. In practice it burns through the script execution time and URL fetch quota that Google Sheets enforces.

When a script overruns, it fails quietly. You are left staring at stale numbers with no error flag to warn you.

Why Ignoring Bid-Ask Spreads Hurts Accuracy

Many free APIs return only a mid price. For real trading or risk work, you need both the bid and the ask.

Our API returns bid, ask, and a timestamp for every instrument. The features page lays out the full OHLC data model.

How We Approach This Problem at RealMarketAPI

We built RealMarketAPI because the existing options were too slow, too expensive, or too narrow for the developers we kept talking to.

The service aggregates prices from leading exchanges and delivers them in under 150 milliseconds, backed by a 99.99% uptime commitment. That combination is what makes a spreadsheet usable for live work rather than after-the-fact review.

What Makes RealMarketAPI Different for Google Sheets Streaming?

There are three ways to connect: REST, WebSocket, and a Telegram Bot, all on the same low-latency backbone.

We also ship official SDK clients for JavaScript, Python, and C#, so the same feed drops into a serverless bridge with a few lines of code. The SDK clients page covers the typed, async-ready basics.

How to Stream Live Forex Rates to Google Sheets API With RealMarketAPI

Create a free account, no credit card needed.
Copy your API key from the dashboard.
Add a short Apps Script function that calls the REST endpoint.
Set a time trigger to pull data every minute.

For sheets that need to move in real time, pair our WebSocket feed with a small bridge running on a Google Cloud function. Our WebSocket candles guide shows a reconnect-safe pattern in Node.js, and the low-latency forex dashboard use case covers the same idea end to end. WebSocket access begins on the Plus plan.

Can RealMarketAPI's Telegram Bot Feed Data Into Google Sheets?

Yes. The Telegram Bot sends prices, charts, and alerts. You can forward those messages to an Apps Script web app through a webhook, then write them into your sheet, giving you an always-on pipeline without polling.

When to Start Using a Dedicated Forex API for Google Sheets

You do not always need a paid plan. A few signals tell you when it is time to move up.

When Should You Upgrade From a Free Plan to a Paid Forex API for Google Sheets?

Upgrade when:

Your sheet needs updates faster than once a minute.
You require historical depth for back-testing.
A free tier's request ceiling keeps blocking your workflow.
You need dependable uptime for a live dashboard or trading bot.

Our Plus plan adds WebSocket streaming, and Pro layers on the Indicators API, the Intelligence API, and higher request limits. The use cases library shows how teams combine live data with sheets for monitoring and analysis.

If your needs are modest, a few pairs refreshed hourly, the free tier is plenty. For anything headed to production, a paid plan with an SLA is the safer foundation.

Frequently Asked Questions

How to get live prices in Google Sheets?

The GOOGLEFINANCE function imports financial data for some stocks and currency pairs, though it can lag by up to 20 minutes. For broader coverage and lower latency, use Google Apps Script to call a dedicated forex API. RealMarketAPI, for example, serves sub-150ms data, with free coverage of six core symbols and 60+ instruments on paid plans.

How to pull live cryptocurrency prices into Google Sheets?

Use a crypto-capable API called from an Apps Script function on a timer. RealMarketAPI covers crypto pairs alongside forex, so a single endpoint can feed both into one sheet.

Can I use WebSocket to stream forex data into Google Sheets?

Not directly, because Sheets cannot hold a socket open. The workaround is a small middle layer, such as a script on a server or a Google Cloud Run service, that receives WebSocket ticks and writes them to the sheet through the Sheets API.

What is the best free forex API for Google Sheets integration?

It depends on your needs. Finnhub gives 60 calls per minute and bundles WebSocket on its free tier. Alpha Vantage adds 50+ technical indicators but caps the free tier at 25 requests per day with no WebSocket. RealMarketAPI offers a no-credit-card free tier at 5,000 requests per month and sub-150ms latency, with WebSocket on paid plans.

How do I handle rate limits when using a forex API in Google Sheets?

Read the response headers, such as X-RateLimit-Remaining and Retry-After, then add Utilities.sleep() between calls or use exponential backoff in your Apps Script. Our API returns clear error codes that tell your script exactly how long to wait.

Does MCP Still Matter in the AI Ecosystem?

Phi Thành — Thu, 21 May 2026 16:45:52 +0000

The AI ecosystem moves fast.
A few months ago, almost every discussion around AI agents included one keyword: MCP (Model Context Protocol).

Today, the conversation feels different.

The hype is quieter.
The excitement is less explosive.
Some developers even started asking:

“Is MCP already declining?”

The short answer is:

Yes, the hype cycle is cooling down. But MCP is still very important.

And in many cases, it may become more valuable precisely because the noise is fading.

The Rise of MCP

MCP was introduced as a standardized way for AI systems to communicate with tools, APIs, databases, and external services.

Instead of building custom integrations for every AI workflow, developers could rely on a common protocol layer.

That idea was powerful.

Very quickly, major companies and tools began adopting it:

Anthropic
OpenAI
Google
Cursor
Windsurf
JetBrains
VS Code ecosystem

Many developers compared MCP to:

REST APIs for AI
USB for agent tooling
Kubernetes for interoperability
Those comparisons may sound exaggerated, but they explain why the ecosystem expanded so aggressively.

Why The Trend Feels Slower Now

The downtrend is real — at least socially.

You can observe it across:

X/Twitter discussions
YouTube trends
Hacker communities
startup pitches
The reason is simple:

1. The “MCP wrapper” era is ending

At one point, many startups simply added:

“MCP-compatible”
“AI agents”
“tool orchestration”
…without solving a meaningful problem.

As the market matured, people became more selective.

Infrastructure alone is no longer enough.

2. Security concerns became more visible

As adoption increased, researchers started discovering real vulnerabilities inside MCP ecosystems.

Some problems included:

unsafe tool execution
prompt injection
poisoned MCP servers
weak permission boundaries
This changed the industry mindset.

Companies moved from:

“How fast can we integrate MCP?”

to:

“How safely can we deploy MCP in production?”

That naturally slowed the hype.

3. Too many low-quality MCP servers

A large portion of public MCP projects are abandoned, duplicated, or low-value.

This is a common pattern in every fast-growing ecosystem.

We saw it with:

npm packages
crypto projects
browser extensions
AI wrappers
Rapid growth creates noise before standards mature.

But MCP Is Still Good — And Here’s Why

The important thing is this:

MCP is shifting from “trend” to “infrastructure.”

That is a very different phase.

1. Standardization still matters

AI agents without standardized tooling quickly become messy.

Without MCP:

every integration becomes custom
portability decreases
maintenance costs increase
agent systems become fragile
MCP solves a real engineering problem:

structured context and tool communication.

That problem does not disappear.

2. Enterprise adoption is still growing

While social hype cooled, enterprise usage continued increasing.

This is an important distinction:

consumer hype may decline
infrastructure adoption may continue quietly
Many successful technologies follow this pattern.

For example:

Docker
GraphQL
Kubernetes
gRPC

The loud phase ends.
The real deployment phase begins.

3. AI agents genuinely need interoperability

The future of AI is likely multi-agent and tool-driven.

Agents increasingly need to:

access databases
execute workflows
communicate with SaaS platforms
retrieve contextual memory
coordinate across systems
MCP provides a practical structure for that ecosystem.

Even critics of MCP often admit that the underlying problem is real.

The Real Future of MCP
MCP may not remain the only protocol.

That is important to understand.

We will probably see:

new standards
enterprise variants
secure extensions
protocol competition
orchestration layers above MCP
But even if the implementation changes, the core idea remains valuable:

AI systems need a universal way to interact with tools and context.

That concept is unlikely to disappear.

Final Thoughts

MCP is no longer in its explosive “gold rush” phase.

The market is becoming more realistic:

fewer buzzwords
more production concerns
more focus on reliability and security
That is actually healthy.

A technology becomes durable when:

hype decreases
practical usage increases
standards stabilize
infrastructure matures
So, is MCP still good?

Yes.

But today, it is less about excitement — and more about discipline, architecture, and long-term interoperability.

And honestly, that may be a stronger foundation than hype ever was.

Inside My Real-Time Market Data API: Architecture, Challenges, Reality

Phi Thành — Mon, 27 Apr 2026 11:01:58 +0000

Last month I shared how I turned a side project into a SaaS. Today, I’m opening the hood and showing you the actual architecture, tech decisions, and lessons learned.

📋 Table of Contents

I. Why I Built This
II. High-Level Architecture
III. Core Components Explained
- 1. RealMarketAPI (Entry Point)
- 2. Auth Service
- 3. RealMarketServices (The Brain)
- 4. Data Storage Strategy
- 5. Data Ingestion
- 6. RealMarketBots (Bonus Layer)
IV. Tech Stack
V. Biggest Challenges So Far
VI. What’s Next
VII. Final Words

I. Why I Built This

I got tired of:

Paying $200–$800/month for APIs that go down during high volatility
Inconsistent latency (especially WebSocket)
Terrible historical data quality
Vendors suddenly changing pricing or throttling indie developers

So I decided to build my own — focused on reliability, transparency, and developer experience.

II. High-Level Architecture

Here’s the current system:

III. Core Components Explained

1. RealMarketAPI (Entry Point)

Simple REST + WebSocket gateway
Handles authentication (JWT + API keys)
Rate limiting & usage tracking

2. Auth Service

Validates tokens
Checks subscription tier
Rejects invalid requests early

3. RealMarketServices (The Brain)

This is where the magic happens. It’s split into three main services:

Ticker Service – Ultra-low latency price updates
Stream Service – WebSocket broadcaster (using Redis Pub/Sub)
Historical Service – Candle & tick data delivery

4. Data Storage Strategy

Redis → Hot short-term ticker data + Pub/Sub
PostgreSQL + TimescaleDB → Long-term candles and relational data
Candles DB → Dedicated time-series optimized storage

5. Data Ingestion

Multiple workers fetch from various Brokers/Exchanges
Retry logic + circuit breakers + fallback sources
Data is normalized into a unified format

6. RealMarketBots (Bonus Layer)

One of my favorite parts:

Scrapes news & trends using RSS + SerpAPI
Generates content summaries with Gemini
Sends Telegram alerts
Auto-publishes to Facebook and X

IV. Tech Stack

Backend: C# .NET

Frontend: Next.js

Real-time: Redis + custom WebSocket server
Database: PostgreSQL
Observability: Datadog

V. Biggest Challenges So Far

WebSocket Scale – Maintaining thousands of concurrent connections with low latency is hard.
Data Consistency – Different exchanges have different timestamp formats and precision.
Cost Control – Exchange API fees + bandwidth can explode quickly.
Testing – You can’t easily replay real market conditions.

VI. What’s Next

TradingView widget
Official SDKs (Python, Node.js, Go)
More symbols & exchanges
Dedicated enterprise nodes

VII. Final Words

Building a market data API taught me one important lesson: Reliability beats features.

Most users don’t need 10,000 symbols. They need 3 symbols that actually work at 3 AM when Bitcoin crashes.

That’s what I’m optimizing for.

Let me know in the comments:

What market data pain point do you have?
Would you use this for algo trading, dashboards, or AI agents?

I read every comment.

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Choosing a Market API in 2026? Read This Before You Decide

Phi Thành — Sun, 19 Apr 2026 06:44:58 +0000

The market data API space in 2026 is more competitive—and more mature—than ever. Most providers now offer similar core features (price feeds, historical data, indicators), so the real differences come down to latency, reliability, pricing, and developer experience.

If you're building a trading bot, fintech SaaS, or analytics dashboard, choosing the right API early can save a lot of headaches later.

📌 Quick Overview

API	Best For	Free Tier	Real-Time
Polygon.io	High-performance trading apps	❌ Limited	✅ Yes
Alpha Vantage	Beginners / learning	✅ Yes	⚠️ Limited
Twelve Data	Global market coverage	✅ Yes	⚠️ Paid
Finnhub	Alternative data + streaming	✅ Yes	✅ Limited
RealMarketAPI	High-throughput SaaS apps	✅ Yes	✅ Yes

1. Polygon.io — High-Performance Real-Time Data

👉 https://polygon.io/

Polygon.io is often used in systems where low latency and real-time execution are critical. It provides tick-level data and WebSocket streaming, making it suitable for trading infrastructure.

Pros:

Real-time WebSocket streaming
Tick-level historical data
Stable infrastructure

Cons:

Can get expensive
Mainly focused on US markets

2. Alpha Vantage — Free and Beginner-Friendly

👉 https://www.alphavantage.co/

Alpha Vantage is a go-to option if you're just getting started. It offers a wide range of data along with built-in technical indicators.

Pros:

Free tier available
50+ technical indicators
Simple REST API

Cons:

Strict rate limits
Delayed data on free plan

3. Twelve Data — Strong Global Coverage

👉 https://twelvedata.com/

Twelve Data is known for supporting a wide range of exchanges, making it useful for apps that need multi-region data.

Pros:

Coverage across many markets
Clean API design
Good documentation

Cons:

Real-time requires paid plan
Some advanced data is limited

4. Finnhub — Real-Time + Alternative Data

👉 https://finnhub.io/

Finnhub combines standard price feeds with alternative datasets like sentiment and insider trading activity.

Pros:

WebSocket support
Alternative data available
Decent free tier

Cons:

Usage limits on free plan
Premium data is paid

5. RealMarketAPI — Simple and High Throughput

👉 https://realmarketapi.com/

If you're exploring a market data API for dashboards or SaaS products, RealMarketAPI focuses on simplicity and handling frequent requests efficiently.

Pros:

Easy integration
Handles high request volume well
Fast responses

Cons:

Smaller ecosystem
Still expanding features

🧠 Final Thoughts

There’s no single “best” API—it depends on your use case:

Need ultra-low latency → Polygon.io
Learning or prototyping → Alpha Vantage
Global coverage → Twelve Data
Alternative datasets → Finnhub
High-frequency SaaS usage → RealMarketAPI

Tip: Test at least 2–3 APIs under real conditions (not just docs). Rate limits and latency behave very differently in production.

💬 Discussion

What API are you using in your project right now?

Any hidden gems worth trying in 2026?

Is Reddit Dead… or Just Not What It Used to Be?

Phi Thành — Tue, 14 Apr 2026 16:24:20 +0000

I’ve been thinking about this a lot lately:

Is Reddit… kinda dead?

Not in the obvious way.
It still has millions of users, huge traffic, and active communities.

But something feels off.

Reddit used to feel like the “last real place on the internet” —
raw opinions, honest feedback, weird niche communities you couldn’t find anywhere else.

Now?

It feels different.

• Discussions feel repetitive
• The same posts get recycled over and over
• Bots and automated content are everywhere
• Moderation is stricter than ever
• And sometimes… it’s just hard to reach real people

You can spend 30 minutes scrolling and walk away feeling like you didn’t actually learn anything new.

At the same time, people still say:
“Reddit is the best place for honest opinions.”

And maybe that’s still true… sometimes.

But it also feels like Reddit is slowly turning into something else:
more controlled, more predictable, less human.

So I don’t know.

💣 Maybe Reddit isn’t dead.

Maybe it’s just… evolving.

Or maybe we’re all just chasing what it used to be.

🧠 Curious what you think:

Is Reddit still one of the most valuable platforms on the internet,
or is it running on its past reputation?

I built an open-source trading RSS directory (inspired by public API lists)

Phi Thành — Sun, 12 Apr 2026 13:05:22 +0000

Hey everyone,

I recently put together a small open-source project that I thought might be useful to others here.

It’s a curated directory of trading-related RSS feeds — covering crypto, forex, stocks, and macro — inspired by projects like public API directories and “awesome lists”, but focused on a more specific niche.

The idea is simple:
create a clean, community-driven place where people can collect, organize, and maintain useful market data sources over time.

🔗 Project:

Landing Page: Here
Github: Here

What it is

Right now, it’s intentionally minimal:

Categorized RSS feeds across major markets
A straightforward structure that’s easy to navigate
Open for contributions and extensions

No complex features, no heavy UI — just something practical that works and can grow with the community.

🌐 Who it’s for

I had in mind:

indie traders who rely on fast information
developers building trading tools or bots
market analysts looking for structured data sources

Basically anyone who prefers working with raw, reliable feeds instead of scattered links.

🚀 Why open source

Projects like public API lists work well because they evolve through contributions.

I’m hoping this can follow a similar path — where people add new sources, improve categorization, or even build tools on top of it.

💡 What could come next

If there’s interest, a few ideas I’ve been thinking about:

feed quality / reliability scoring

tagging or filtering systems
API layer for programmatic access
integration with trading tools or dashboards
Contributing

If you know good RSS sources, or just want to help shape the structure, contributions are welcome.

Even small additions (one good feed) can make it more useful for everyone.

Would love to hear your thoughts or feedback 🙌

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Is MCP Worth It for Algorithmic Trading? The Reality Behind Data, Latency, and Execution

Phi Thành — Sat, 11 Apr 2026 04:59:21 +0000

If you're building in algo trading, you've probably come across MCP (Market Control/Connectivity Platforms) or similar infrastructure layers promising cleaner data access, unified feeds, and faster execution.

But the real question is:

👉 Is it actually worth it?

The Reality Most People Don’t Say

MCP sounds great on paper — unified APIs, standardized data, lower integration overhead.

But in practice, for algo trading, your edge rarely comes from how clean your API looks.

It comes from:

Data quality (tick accuracy, gaps, consistency)
Latency (especially for lower timeframes)
How you structure and interpret price behavior

And this is where MCP can fall short.

Most MCP-style systems:

Add abstraction layers → more latency
Normalize data → sometimes lose important microstructure details
Lock you into their ecosystem

That’s fine for dashboards or retail tools.

But for serious trading systems? It can become friction.

When MCP Does Make Sense

To be fair, MCP is useful if:

You’re building a multi-asset dashboard
You need quick integration across brokers/exchanges
You don’t care about millisecond-level execution
You’re validating ideas, not optimizing execution

In these cases, speed of development > precision.

Where It Breaks for Algo Trading

If you're doing:

Price action modeling
Order flow approximation
Volatility-based logic
Structural/swing analysis

Then small inconsistencies matter a lot.

Things like:

Feed delays
Candle reconstruction differences
Missing ticks

…can completely distort your system over time.

My Approach (and Why I Built My Own Stack)

Instead of relying fully on MCP, I’ve been working on a hybrid approach:

Direct market feeds where possible
Custom structuring logic on top (price action, swing points, contextual behavior)
Focus on consistency over abstraction

That’s actually why I built:

👉 RealMarketAPI/MCP

It’s not trying to be a generic MCP.

It’s designed more like:

A clean, consistent data layer
Focused on traders building systems (not just displaying charts)
With endpoints like volatility, multi-symbol coverage, and structured data ready to plug into strategies.

The Bottom Line

MCP is not “bad” — it’s just often misaligned with serious algo trading needs.

If your goal is:

Fast prototyping → MCP is fine
Production-grade trading systems → you’ll likely outgrow it

At some point, you’ll care less about ease of integration and more about:

“Can I trust this data to drive real decisions?”

That’s where most abstractions start to break.

Curious how others are approaching this.

Are you using MCP, direct feeds, or something in between?

Has the Product Hunt Era Ended?

Phi Thành — Sun, 05 Apr 2026 16:22:05 +0000

There was a time when launching on Product Hunt felt like a rite of passage.

You’d spend weeks preparing:

polishing your landing page
lining up upvotes
messaging friends and communities
timing the launch down to the minute

And for a while… it worked.

You could get thousands of visitors in a day. Early users. Feedback. Sometimes even revenue.

But lately, something feels different.

The Signal Feels Weaker

Product Hunt hasn’t disappeared. It’s still active, still full of launches every day.

But the impact?

It’s diluted.

More products. More noise. More “launch strategies” that look almost identical. What used to feel like discovery now feels… expected.

A top 5 product today doesn’t carry the same weight it did a few years ago.

The Audience Changed

Early Product Hunt was full of builders, early adopters, and curious tech people.

Now it’s broader. Which isn’t a bad thing — but it changes the dynamic.

Less curiosity, more scrolling.
Less engagement, more passive consumption.

Launching there no longer guarantees meaningful feedback or real users.

Distribution Has Moved Elsewhere

If you look at where traction actually happens today, it’s fragmented:

X (Twitter) for real-time conversations
niche communities (Discord, Reddit, Slack groups)
SEO and long-term content
direct audience building

Instead of one big launch moment, growth now comes from consistent presence.

Launch Day vs Build in Public

The biggest shift isn’t Product Hunt itself.

It’s how people build.

Before:

Build in private → launch big → hope it works

Now:

Build in public → share progress → accumulate users over time

By the time you “launch,” people already know your product.

Product Hunt becomes optional, not essential.

So… Is It Over?

Not exactly.

Product Hunt is still useful:

for visibility
for backlinks
for credibility (to some extent)

But it’s no longer the growth engine.

It’s just one channel among many.

What Actually Works Now

If you’re building something today, the playbook looks different:

Talk about your product early
Share real progress (not just polished updates)
Engage with your niche audience
Focus on retention, not just traffic spikes

Because a quiet 100 users who care > 10,000 visitors who bounce.

Maybe the Product Hunt era didn’t end.

It just… matured.

And the builders who adapt are the ones who win.

🙂 I Started Giving Myself “Happiness Points” Every Day (And It Changed More Than I Expected)

Phi Thành — Sat, 04 Apr 2026 14:47:04 +0000

I Started Tracking My “Happiness Points” Every Day

I didn’t start this as a project.

I just noticed that a lot of my days felt the same. Not bad, not great — just… flat. After a while, it became hard to tell if things were actually improving or not.

So I tried something simple.

The Idea

At the end of each day, I give it a small score based on how I felt:

+2 → really good day
+1 → decent
0 → neutral
-1 → low energy
-2 → rough day

No long journaling. No deep analysis. Just a number.

Why I Kept It This Simple

I’ve tried more detailed tracking before — mood apps, journaling, habit trackers. They usually work for a few days, then I stop.

This takes a few seconds, so it’s easy to keep going.

What I Built

I made a small page that stores the score locally in the browser.

const logMood = (score) => {
  const logs = JSON.parse(localStorage.getItem("moodLogs") || "[]");

  logs.push({
    date: new Date().toISOString(),
    score
  });

  localStorage.setItem("moodLogs", JSON.stringify(logs));
};

That’s basically the whole thing.

What I Noticed After a While

After a week or two, patterns started to show up.

Days with enough sleep were usually +1 or +2
Too much time on my phone often led to 0 or -1
Working long hours didn’t always mean a better day
Going outside, even briefly, helped more than expected

None of this is surprising on its own. But seeing it reflected in my own data made it more concrete.

The Useful Part

It didn’t suddenly make me happier.

But it made things easier to notice.

Sometimes during the day I’d catch myself thinking:

this is probably going to end up as a -1

And that small thought was enough to adjust something — take a break, go outside, or stop working earlier.

What I Didn’t Add

I didn’t add:

reminders
streaks
analytics
syncing

Because I know I wouldn’t stick with it if it became complicated.

If You Want to Try It

You don’t need to build anything.

You can just write it down somewhere:

Apr 4 → +1  
Apr 5 →  0  
Apr 6 → -1

Do it for a week and see if anything stands out.

Final Thought

This isn’t about measuring happiness perfectly.

It’s just a small way to stay aware of how your days are going.

And for me, that’s been enough to make small adjustments over time.

Build a Chrome Extension to Check Real-Time Gold Price (Beginner Friendly)

Phi Thành — Wed, 01 Apr 2026 15:57:00 +0000

If you’ve ever traded, tracked markets, or just got curious about gold prices, you probably know how annoying it is to constantly open tabs just to check one number.

So instead, let’s build a simple Chrome extension that shows the real-time gold price right in your browser.

No fluff. No overengineering. Just something useful.

🧠 What We’re Building

A lightweight Chrome extension that:

Fetches gold price (XAUUSD)
Displays it in a popup
Updates on demand (or periodically if you want)

⚙️ Step 1: Create the Extension Structure

Create a folder:
gold-price-extension/

Inside it:
manifest.json
popup.html
popup.js

📄 Step 2: `manifest.json`

{
  "manifest_version": 3,
  "name": "Gold Price Checker",
  "version": "1.0",
  "description": "Check real-time gold price",
  "action": {
    "default_popup": "popup.html"
  },
  "permissions": ["storage"]
}

Step 3: UI `popup.html`

<!DOCTYPE html>
<html>
<head>
  <title>Gold Price</title>
  <style>
    body {
      font-family: Arial;
      padding: 10px;
      width: 200px;
    }
    #price {
      font-size: 18px;
      font-weight: bold;
    }
  </style>
</head>
<body>
  <div>Gold Price (XAUUSD)</div>
  <div id="price">Loading...</div>
  <button id="refresh">Refresh</button>

  <script src="popup.js"></script>
</body>
</html>

Step 4: Fetch Data `popup.js`

You can use any market data API here. There are many options out there — paid, free, aggregated, etc. Pick what fits your needs.

For this example, we’ll use RealMarketAPI because it’s lightweight and the free tier (5k requests/month) is more than enough for something like this.

Before running the extension, make sure you have an API key.

Sign up with any market data provider you prefer
Generate your API key
Replace API_KEY in the code

const priceEl = document.getElementById("price");
const refreshBtn = document.getElementById("refresh");

async function fetchPrice() {
  priceEl.innerText = "Loading...";

  try {
    const res = await fetch(
      "https://api.realmarketapi.com/api/v1/price?apiKey=API_KEY&symbolCode=XAUUSD&timeFrame=H1"
    );

    const data = await res.json();

    priceEl.innerText = `$${data.closePrice}`;
  } catch (err) {
    priceEl.innerText = "Error fetching price";
    console.error(err);
  }
}

refreshBtn.addEventListener("click", fetchPrice);

// load on open
fetchPrice();

📦 Example API Response

{
  "symbolCode": "XAUUSD",
  "openPrice": 4738.27400000,
  "closePrice": 4739.83000000,
  "highPrice": 4746.85500000,
  "lowPrice": 4724.20200000,
  "volume": 6365,
  "openTime": "2026-04-01T14:00:00.000Z",
  "bid": 4739.83,
  "ask": 4740.046
}

You can display more than just closePrice if you want:
Bid / Ask
High / Low
Volume

🚀 Step 5: Load the Extension

Open Chrome
Go to: chrome://extensions/
Enable Developer mode
Click Load unpacked
Select your folder

Done.

🔄 Optional: Auto Refresh

If you want it to update automatically:

setInterval(fetchPrice, 60000); // every 60s

✅ Conclusion

That’s it — a simple Chrome extension that gives you real-time gold prices without needing to open a single tab.

It’s small, but practical. And more importantly, it shows how easy it is to turn raw API data into something you actually use every day.

From here, you can go as deep as you want — add more assets, build charts, set alerts, or even turn it into a full trading dashboard.