How A Trading Insight Boosted Profits

[alanvito1]

How A Trading Insight Boosted Profits

Category: Profit Management
Date: 2025-08-01

Introduction

In the fast-paced world of algorithmic trading, small insights can lead to significant profit boosts. The Orstac dev-trader community has uncovered one such insight: leveraging time-based volatility patterns to optimize trade execution. By combining programming expertise with market intuition, traders can turn these patterns into actionable strategies.

For those new to algo-trading, tools like Deriv and community discussions on Telegram are invaluable. Deriv’s platform offers robust APIs for testing strategies, while Telegram groups provide real-time collaboration. This article explores how a simple yet powerful insight—tracking volatility spikes—can enhance profitability, with practical steps for programmers and traders alike.

Subsection 1: Identifying Volatility Patterns With Code

Volatility isn’t random; it often follows predictable intraday patterns. For example, asset prices tend to spike during market openings and major news events. By coding a script to detect these moments, traders can avoid losses or capitalize on opportunities.

Here’s how to implement this:

• Use Python or JavaScript to fetch historical volatility data (e.g., from GitHub repositories like ORSTAC’s open-source toolkit).
• Apply a simple moving average (SMA) to volatility indicators like ATR (Average True Range).
• Trigger trades when volatility crosses a threshold, such as 1.5x the SMA.

“Volatility clustering is a well-documented phenomenon. Markets exhibit periods of high and low volatility, often driven by external events or liquidity changes.” — Advances in Financial Machine Learning by Marcos López de Prado

For a no-code approach, try Deriv’s DBot platform, which lets you drag-and-drop logic blocks to build volatility-based strategies. Think of it like a traffic light: green for high volatility (enter trades), red for low volatility (hold or exit).

Subsection 2: Optimizing Trade Execution

Identifying volatility is half the battle; executing trades at the right time is the other. Slippage—the difference between expected and actual trade prices—can eat into profits during volatile periods. Here’s how to mitigate it:

• Use limit orders instead of market orders to control entry/exit prices.
• Split large orders into smaller chunks to avoid moving the market.
• Backtest strategies using Deriv’s synthetic markets to simulate real-world conditions.

Imagine you’re filling a glass of water. Pouring too fast (market orders) causes spills (slippage). Pouring slowly (limit orders) ensures precision.

Conclusion

A single insight—volatility pattern recognition—can transform trading performance. By combining coding skills with platforms like Deriv and community resources like Orstac, traders can turn theory into profit. Whether you’re a programmer writing scripts or a trader refining execution, small tweaks yield big results. Start small, test often, and let data guide your decisions.