Hello everyone,
Over the past ~8 months I’ve been developing a BTC trading system, with the primary emphasis not on maximizing short-term returns, but on risk structure, capital preservation, and robustness across different market conditions.
Preliminary tests on multiple altcoins show broadly comparable behavior, with no obvious degradation in risk characteristics, though BTC remains the primary focus.
Due to TradingView’s reporting limitations, performance results are reviewed and exported on a monthly basis.
In this thread, I’m sharing multiple consecutive months of Strategy Tester reports (from November up to now), each posted separately as
screenshotsClarifications (to avoid confusion)
The core trading logic has remained unchanged across all months
Execution details, internal safeguards, and implementation quality were gradually refined
The risk framework and structural constraints stayed consistent
Position sizing and capital management embedded directly in the strategy
All trades have predefined Stop Loss and Take Profit
No leverage applied (leverage logic is currently under development)
Trading commissions are included
Slippage limited to TradingView’s default model
When TP / SL / Commission parameters are modified:
- Entry points shift
- Profitability may decrease
But the system does not transition into unstable or high-risk regimes
Observed Metrics (Based on Multi-Month Backtests)
Profit Factor:~ ranges between 2.24 and 8.2
Win Rate:~ 52%–78%
Maximum Drawdown:~ observed between 0.6% and 3.6%
These figures are presented for context, not as performance claims.
Questions for Experienced Members
At this stage, I’m deliberately not asking whether the strategy ‘has an edge’, but rather where such an edge could realistically fail when exposed to real-world execution and scaling constraints.
I’m particularly interested in insights from those who’ve scaled beyond personal systems:
1.TradingView Backtests
- How much weight do you personally assign to TradingView Strategy Tester results as an early-stage falsification tool, rather than final validation?
- What discrepancies have you observed between TradingView backtests and live execution?(fills, latency, slippage, behavioral drift)
2.Scaling & Crowding Effects
I want to be precise here and distinguish between different meanings of “scaling”:
Capital Scaling: increasing position size while keeping logic unchanged
Distribution Scaling: multiple users or accounts running identical logic
Execution Scaling: migration from platform-based execution to direct exchange APIs
From your experience:
- At what point does capital size begin to materially affect execution quality in liquid BTC markets on lower timeframes?
- When does shared logic (distribution) become a practical concern, if at all, assuming no public signal broadcasting?
- At what stage does a TradingView-based system meaningfully benefit from transitioning to a custom execution layer?
3.Productization & Confidentiality
- Any practical or security concerns with TradingView-based systems?
- Have you experienced edge leakage or degradation as systems scale?
4.Open Critique
- Based purely on the data, what appears fragile or insufficient?
- What would still prevent you from trusting or deploying such a system at scale?
Closing Note
This thread is not about selling signals or claiming edge.
The goal is to stress-test both the data and the assumptions before anything moves beyond controlled experimentation.
Any technical critique, skepticism, or shared experience, especially from those who have transitioned from personal systems to scaled environments is very welcome.
