Building Taciturn — What I've Learned Building an Algo Trading System From Scratch

Where It Started

Taciturn started whilst I was starting out trading, the idea came in the vision of a terminal that could give me financial insights on what the market of my choice was doing, whether it was near the daily high or daily low, and after coding a trend manager i could receive financial call outs via this terminal, telling me to buy or sell there was alot of other infomation which id look at usually whilst trading such as session deltas, yesterdays highs and lows, data id usually look at manually this was about 700 lines of code no broker integration no execution

The next version was to put this on a gui which would scan for patterns using candlesticks, then would give me an outlook based on strong buy, buy, sell or strong sell, given the bias an hourly breifing would occur telling me what ive missed, this was during the time i was busy with work and would often miss market movmements due to other commitments.

Taciturn which was previously called ‘SPS’ for silver processing system, was getting really good at predicting market movements, I would start acting accordingly to what the system has prompted, which lead to many sucessful trades so much so I wanted to automate it, however this was a much bigger task than I thought it would be after having no prior coding knowledge.

How It Evolved

The project grew in phases. Each one introduced a new broker, a new strategy, or a new layer of infrastructure:

v1–3 (Alpaca, paper trading) — ETF trading on GLD and SLV using candlestick pattern recognition on daily bars. Win rates looked promising on backtests — 60–80% on some patterns. Then I moved to intraday bars and they collapsed to 14–45%. Patterns that work on daily data often don’t survive the noise of shorter timeframes. That was a costly discovery.

v4 (OANDA, spot forex) — Migrated entirely to OANDA for XAU/USD and XAG/USD spot forex. Rebuilt the signal stack around indicator-based approaches: volume-weighted momentum crossovers, MACD histogram signals, pure indicator models. Added a trend filter that checks the hourly EMA slope before every trade — if the trend is flat or against you, it blocks entry.

v5 (current) — Multiple signals running in parallel, risk management with broker-level stop losses on every order, a full web dashboard, and Pushover notifications. The system runs continuously, manages its own cooldowns, and halts itself if daily losses exceed a threshold.

The most up to date gui, in the early days of development

What I’d Do Differently

A lot. In rough order of importance:

• Demo mode for three months before any real capital. I jumped into live-adjacent testing too early. Bugs that seem minor in development become expensive in production.
• Build the backtest engine first. Every signal I’ve added has had to be validated retrospectively. A proper backtest framework would have saved weeks.
• Pick one broker and stay there. Migrating mid-project is expensive in time and introduces subtle bugs — currency conversion errors, API differences, state management issues.
• Only trade gold. Gold is liquid, well-documented, and moves in ways that technical signals can capture. Silver is noisier. Crypto is a different category entirely.
• Forecast bugs before they’re expensive. Duplicate trades from race conditions. Stop losses set at the wrong scale. Currency conversions applied twice. All of these happened and all of them were avoidable with more careful design upfront.

Where It Is Now

The system is standing around £4,400 all-time after three weeks of live demo trading. The goal is consistent profitability before deploying real capital — and a migration to a dedicated Mac Mini for permanent 24/7 operation.

Currently executing trades perfectly with an incredibly high win rate which is perfect, the profit ratio is something we need to work on which can be adjusted and finetuned with the order quantity and stop loss, which is currently set at -£75 and the take profit is +£10, so one trade going badly will wipe out 7 trades, so out of the 2 trades that went badly from the most recent 50 gold trades that leaves only 36 profitable trades, this is something I am actively working to fix.

The target was 85% profitable trades. That’s not realistic for most strategies — but 60–70% with a good risk/reward ratio is, and that’s what the current signal stack is being tuned toward.

However, as of april 9th for the past few days weve had well over 80% with highs of 96% win rate on one day and achieveing ~90% win rate on following days, this win rate is something i believe will hopefully continue in the coming days and weeks, as opposed to a few good days.

More updates to follow.

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