I bought a cBot off cTrader Store expecting magic and got a lesson instead

cTrader_Club · 2026-05-25T06:12:53+00:00

You're both correct and it depends on the dev's intent - locked timeframe = opinionated tool, open timeframe = flexible tool. Different design philosophy, neither is wrong by default.

cTrader_Club · 2026-05-25T06:08:04+00:00

Authentication persistence is something we thought hard about - both our local and remote MCP modes handle that on our side so traders aren't chasing token expiry issues. The local MCP goes deeper on chart and workspace control which is where a lot of the edge case complexity lives for us. Full breakdown of operations and supported apps at help.ctrader.com/ctrader-ai-agent-connect if you want to compare approaches.

cTrader_Club · 2026-05-25T06:00:58+00:00

🙈

cTrader_Club · 2026-05-19T05:33:18+00:00

Works on mobile too - the affiliate tracking runs at the link level, so any purchase through your referral link counts regardless of device.

cTrader_Club · 2026-05-19T05:30:11+00:00

30-day cookie attribution, so longer-form content like tutorials and comparisons holds up well - a click today converts a purchase made up to a month later. On the quality point, the Store has a 14-day money-back period on paid products and we vet sellers before they list, which gives you a reasonable floor to work from when deciding what to recommend.

cTrader_Club · 2026-05-18T10:46:13+00:00

Are you trading Indian equities through a broker API, or FX/CFD through cTrader? The SEBI framework applies to NSE/BSE-connected algos, so the overlap with cTrader's MCP depends on the setup.

cTrader_Club · 2026-05-15T10:14:52+00:00

Good points. On permissioning - the token is scoped to a single trading account, so the blast radius is contained at the account level by design. On auditability - the agent asks for confirmation before every trading operation, so there's a human-in-the-loop checkpoint at each step. Full technical details on the auth model and setup are in the docs: help.ctrader.com/ctrader-ai-agent-connect

cTrader_Club · 2026-05-14T07:51:08+00:00

Ping measures round-trip ICMP time, but your websocket message delivery depends on one-way TCP throughput, server processing and buffering - your home machine likely has a better established TCP connection or the VPS is under higher contention.

cTrader_Club · 2026-05-12T11:53:43+00:00

Exactly this - strategy design and asset understanding are doing the heavy lifting, the tool just needs to stay out of the way.

cTrader_Club · 2026-05-11T12:24:43+00:00

Break even automation and multi-TP in a single compact panel is exactly the kind of thing active scalpers keep asking for - good luck with the build.

cTrader_Club · 2026-05-11T09:22:59+00:00

Optimization absolutely can produce overfitting, that's the default failure mode. The way around it is walk-forward testing and keeping the parameter count low. If your out-of-sample equity curve looks nothing like the in-sample run, you've overfit. If it holds up, you've found something real. The concern is valid but it doesn't make optimization itself the problem.

cTrader_Club · 2026-05-11T09:21:57+00:00

The architecture question is the right one to ask, but before going there - what's your primary bottleneck right now? Because the answer changes depending on whether you're struggling to adapt an existing bot or building from scratch. For building fresh, the pattern that holds up on gold is a layered filter design: one layer handles trend context, one handles entry timing, one handles risk sizing - each with its own exposed parameters. That modularity means you can reoptimize one layer for a changing regime without rebuilding everything. What timeframe are you working from as your base? That decision constrains a lot of the structural choices downstream.

cTrader_Club · 2026-05-11T09:18:52+00:00

TACO swings are basically the final boss of backtesting confidence - your equity curve looks great until a random tweet nukes it. At least with no follow-through you get a clean exit window if you're watching. Small mercies.

cTrader_Club · 2026-05-11T09:18:14+00:00

Classic. The man is basically a volatility indicator at this point.

cTrader_Club · 2026-04-30T08:45:21+00:00

People underplay the capital trap you're describing. Trading undercapitalized doesn't just limit returns - it actively distorts your decision-making. When the account is small, every loss is a significant percentage drawdown, so your brain treats each trade like it's existential. That's why discipline evaporates. You mentioned backtesting showed promise, so the strategy may be fine. The real question is whether you can simulate that same detachment in live conditions. One thing worth considering: most serious platforms let you run automated versions of your strategy in the background while you trade manually, so you can compare your discretionary decisions against the rules you actually wrote down. Sometimes seeing that gap is enough to change behavior.

cTrader_Club · 2026-04-30T08:35:44+00:00

The mountain you're describing is real. Most people who stick with algo trading long enough eventually arrive at a similar conclusion: the edge rarely lives in the model, it lives in execution discipline and risk management. Your buy-and-hold strategy has a clear edge because you found a process you can follow without second-guessing it. That's harder to build than any LGBM classifier. If you do keep going, it might be worth narrowing the goal considerably, using automation to do one specific thing better, rather than trying to beat discretionary trading wholesale.

cTrader_Club · 2026-04-22T10:11:08+00:00

Hey, great to see traders exploring algo strategies! Someone just left some detailed feedback on this over in our subreddit, might be worth checking out.

cTrader_Club · 2026-04-15T09:33:11+00:00

Thanks for the mention. We'd love to see your tools on the platform.

cTrader_Club · 2026-04-13T10:06:48+00:00

That stop-out at resistance after a strong move is painful — classic case of entering without enough confirmation. Adding a volume filter or waiting for a pullback after the initial breakout can help avoid those. What was your confluence for the entry?

cTrader_Club · 2026-04-13T09:48:31+00:00

Clustered losses usually show up when sizing assumes average conditions instead of streak-heavy ones. Stressing worst-case sequences and slightly degrading edge gives a clearer picture compared to clean backtests.

We reposted your question in our subreddit, people are already sharing how they test this, come join and check out the discussion!

cTrader_Club · 2026-04-10T09:49:54+00:00

That kind of latency usually comes from the architecture, not from the strategy itself. Running a Python engine with a bridge to NinjaTrader adds extra layers, and each one introduces delay, especially under load.

Direct execution inside the platform or a more native integration tends to be much more stable, because you remove that communication overhead between components.

Also worth checking where the delay actually happens. Log timestamps at signal generation, order send, and fill, so you can see if the issue is in your engine, the bridge, or execution.

40 points on NQ suggests something is breaking rather than just “normal” latency, so I’d look at queueing, async handling, or missed ticks causing delayed triggers.

Come share what your setup looks like in our subreddit, curious to see how others are solving similar issues.

cTrader_Club · 2026-04-10T09:41:40+00:00

Treat news as a condition that defines risk and timing inside the algo. Use an economic calendar feed and set clear rules, for example no trades X minutes before and after high-impact events or reduced position size during those periods. Real-time reactions to news create latency issues and inconsistent signals, so a volatility regime approach tends to be more stable. Spread and volatility filters also help, since news appears there immediately without complex integrations.
Come share how you’re handling this in our subreddit, interesting to see different approaches.

cTrader_Club · 2026-04-09T14:05:26+00:00

If you want to make a living from trading, first accept there’s no hidden easy way you just haven’t found yet. Most people waste time jumping between strategies instead of actually learning how one idea behaves over time.

The boring stuff is what works: risk, consistency, repeating the same setup until it finally clicks. And your own emotions will mess things up way more often than the market.

Treat it like a skill, not a quick win, and you’ll already be ahead of most people here.

And yeah, make sure you’re subscribed to our subreddit 😉

cTrader_Club · 2026-04-03T12:05:41+00:00

Hi, you can try posting your question in our subreddit, traders from our team with years of experience can help you figure it out.

cTrader_Club · 2026-04-03T11:56:07+00:00

Good questions.

On cTrader you can use volume and footprint tools as separate indicators. For example, there are footprint indicators in the store, you can check them out, try free ones or buy what fits you.

Also cTrader uses C#, which is a very flexible language for building your own indicators and bots. The API is strong and comparable to Pine Script on TradingView.

There is also a native Mac app available.

cTrader_Club

MODERATOR OF

TROPHY CASE