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Safety orders rescue losing trades, which inflates win rate and hides risk. Freya's backtest shows how your DCA ladder really performed and what it cost you.
Bar close waits for a confirmed candle, intrabar acts the moment a level is touched. How each mode fires the same RSI condition and why it changes your fills.
Chase Take Profit lets a strong move run past your target instead of capping it. How the trigger and the maximum distance work together to lock in trend gains.
On Freya's marketplace, your creator rank decides how many buyers see your strategy. How the visibility boost works and how to climb it with proven listings.
A backtest without slippage and fees is a fantasy. How Freya's engine applies slippage, fill buffers and trading fees so results match live trading costs.
R-multiple measures each trade in units of risk, and expectancy turns it into your edge per trade. Read both in a Freya backtest to see if a strategy pays.
Design DCA safety orders on Freya: each order has its own distance and volume instead of step scale multipliers, so you average down without over-committing.
A bot can thrive in calm markets and fail in volatile ones. Freya's backtest groups trades by ATR volatility so you see the conditions your strategy needs.
Engineering lessons from building a trading bot platform: quiet failures, simulators that flatter you, precision as a domain and constraints that remove work.
Three exchanges, three incompatible minimum order rules, and a bug from inventing a rule one of them never had. What precision work looks like in production.
Why we refuse API keys that carry withdrawal permission instead of just promising not to use it, and what that constraint removes from the system.
Two React traps that only hurt when a field holds money: the number input mouse wheel, and a portalled Radix popover that swallows every keystroke in a dialog.
