If you are reading this then most probably you are a developer and most probably you have experience with IDEs. IDEs have become indispensible for developers, where smart intellisense suggestions simplify your life. I wanted that same magic for my web app's search functionality. Here's how I built an intelligent search system using CodeMirror on the frontend and F# doing the heavy lifting on the backend.

The Problem​

I wanted users to filter cryptocurrencies using natural language like name:Bitcoin marketCap>1B OR ticker:ETH. I wanted search to be smart enough to suggest completions, highlight syntax errors, and handle complex queries without breaking.

After few iterations I arrived at the reasonable model. Keep minimal logic with syntax highlighting based on codemirror on frontend, and use F# where it shines, for both intellisense and expression translation into AST.

Codemirror is my favourite go-to editor, which is heavily customizable and browser-based. You could compare it to vscode, which is very heavy and you would have a hard time customizing it into a single line text-like field.

Crypto portfolio diversification significantly improves risk-adjusted returns across multiple trading strategies. Our data shows diversified portfolios outperform single-asset holdings with higher Sharpe ratios, reduced drawdowns, and in some cases, superior absolute returns.

We have written multiple articles on trading strategies, such as Macd Indicator, Short-term RSI Reversal, and Trend Following. A common theme across these strategies has been the universal improvement of risk-adjusted results when we include additional assets through crypto portfolio diversification. This is not a coincidence, and implementing proper crypto asset allocation across multiple cryptocurrencies can substantially enhance your trading results.

This strategy is based on a trend-following indicator designed to capture sustained price moves. However, like most trend-following tools, it may generate false signals during sideways or low-volatility markets.

This strategy is based on short-term mean reversion — buying when assets become oversold and exiting during recovery. While sometimes confused with scalping, this approach operates on a daily timeframe, making it more of a fast swing strategy than an intra-day one.

I had been using helix for around a year before I finally switched from rider to helix for F# programming. Initially, I used helix only for editing custom text files without any Language Server Protocols (LSPs).

Then I discovered how easy it is to set up LSP for virtually any text format you can think of, such as docker compose, json, yaml, markdown, or even english grammar.

And that's not even mentioning LSPs for programming languages—I now wonder how I could have lived without them before.

Bitcoin as a prominent cryptocurrency has experienced unprecedented growth in the past decade. It has become an accepted asset class to diversify investor returns (along with some other major cryptocurrencies). The question is whether using simple trading rules can improve the risk-adjusted return of such a portfolio.

Log returns are a very useful concept when working with timeseries. It turns out given statement is even more true when working with time-series database and materialized views in clickhouse.

You are now able to filter screener by non-numeric fields, such as when the crypto-currency started from, or if it is still active.

We added a new functionality, where users can upvote or add new feature requests.

I will describe here how in CryptoQuant I architected parsing user-input trading rules into a machine runnable AST.

This parsing system forms the critical bridge between human-readable trading strategies and executable code that can efficiently process market data - ultimately allowing our users to automate sophisticated trading strategies without requiring deep programming expertise.