We ran simulated live trades using the AlgoZilla Omniscius v4.2 Theros model for XLM - Stellar. The results below give you an overview of the most important Key Performance Indicators, Equity Curve and the Trade Log as simulated by our Pythia model. The returns have been accumulated over the February 2025 period. The simulated paper trades of the model show that an initial investment at the start of the period with 19 trades would have compounded to a final value of $17,341 (from a $10,000 base). All results are net of trading fees based on Bitvavo rates (0.15% entry + 0.25% exit). The model uses a 15‑minute delay on the signal on both entries and exits to simulate possible slippage when executing orders.
Stellar (XLM) is an open-source network for cross-border payments and asset tokenization, originally forked from Ripple. With partnerships including MoneyGram and various central banks exploring CBDCs, XLM price responds to payments industry developments. Its lower volatility compared to other altcoins provides steadier trading patterns with reliable mean-reversion characteristics.
Stellar (XLM) — clean monthly entry, no carry-over from previous months. Net of trading fees (Bitvavo 0.15% + 0.25%).
Same coin, same period, same data window — different model. All walk-forward validated. v5.0 (Kairos) is the current production model (live since 11 May 2026); v4.2 (Theros) was the predecessor; v4.0.1 (Pythia) and v4.0 (Itzamná) confirmed the statistical edge during 2025; v2.5 is the legacy baseline.
Δ-arrows on the counterpart cards show this page minus that version. Higher return / WR = better; smaller (less negative) Max DD = better.
1H candles for XLM over the test window with every entry (▲ green up-arrow) and exit (▼ green = profitable, ▼ red = loss). Hover a marker for trade details.
Aggregates derived from the full 19-trade log of this period.
How the model performs across the 4 market regimes detected by Omniscius.
| Regime | Trades | Win Rate | Avg PnL | Compounded Return | Avg Hold |
|---|---|---|---|---|---|
| SIDEWAYS | 16 | 81.3% | +3.04% | +60.72% | 15h |
| BULL | 1 | 100.0% | +3.47% | +3.47% | 10h |
| BUBBLE | 2 | 50.0% | +0.66% | +1.31% | 4h |
All 19 trades executed during this paper trade period.
| Entry | Exit | Entry Price | Exit Price | PnL | Regime | Duration |
|---|---|---|---|---|---|---|
| 03 Feb 2025 01:00 | 03 Feb 2025 10:00 | $0.33 | $0.34 | +3.47% | BULL | 10h |
| 03 Feb 2025 23:00 | 04 Feb 2025 01:00 | $0.37 | $0.37 | -0.46% | BULL | 3h |
| 04 Feb 2025 08:00 | 04 Feb 2025 11:00 | $0.34 | $0.35 | +1.78% | BUBBLE | 4h |
| 05 Feb 2025 19:00 | 06 Feb 2025 06:00 | $0.33 | $0.34 | +3.46% | SIDEWAYS | 12h |
| 06 Feb 2025 08:00 | 06 Feb 2025 10:00 | $0.34 | $0.34 | +0.09% | SIDEWAYS | 3h |
| 06 Feb 2025 19:00 | 06 Feb 2025 23:00 | $0.32 | $0.32 | -0.16% | SIDEWAYS | 5h |
| 07 Feb 2025 01:00 | 07 Feb 2025 13:00 | $0.32 | $0.34 | +5.42% | SIDEWAYS | 13h |
| 07 Feb 2025 23:00 | 08 Feb 2025 23:00 | $0.33 | $0.33 | +1.58% | SIDEWAYS | 1d |
| 09 Feb 2025 22:00 | 10 Feb 2025 13:00 | $0.31 | $0.32 | +4.46% | SIDEWAYS | 16h |
| 10 Feb 2025 15:00 | 11 Feb 2025 06:00 | $0.32 | $0.34 | +6.03% | SIDEWAYS | 16h |
| 12 Feb 2025 01:00 | 13 Feb 2025 01:00 | $0.32 | $0.33 | +4.32% | SIDEWAYS | 1d |
| 13 Feb 2025 03:00 | 14 Feb 2025 10:00 | $0.33 | $0.35 | +6.54% | SIDEWAYS | 1.3d |
| 14 Feb 2025 13:00 | 14 Feb 2025 15:00 | $0.35 | $0.35 | -0.01% | SIDEWAYS | 3h |
| 18 Feb 2025 09:00 | 18 Feb 2025 13:00 | $0.32 | $0.33 | +0.82% | SIDEWAYS | 5h |
| 18 Feb 2025 16:00 | 20 Feb 2025 01:00 | $0.32 | $0.34 | +4.94% | SIDEWAYS | 1.4d |
| 21 Feb 2025 20:00 | 22 Feb 2025 17:00 | $0.32 | $0.34 | +3.89% | SIDEWAYS | 22h |
| 23 Feb 2025 13:00 | 23 Feb 2025 17:00 | $0.33 | $0.33 | -0.20% | SIDEWAYS | 5h |
| 25 Feb 2025 09:00 | 25 Feb 2025 21:00 | $0.28 | $0.30 | +3.98% | SIDEWAYS | 13h |
| 26 Feb 2025 19:00 | 27 Feb 2025 10:00 | $0.28 | $0.29 | +3.40% | SIDEWAYS | 16h |
A paper trade simulates real signal execution without real money. Each period starts fresh with a clean $10,000 allocation — no carry-over from previous windows. This shows exactly what would have happened if you started following signals on day one of that window. View live XLM signals →
Yes. All results are net of trading fees based on Bitvavo rates (0.15% entry + 0.25% exit). No slippage is applied. Trading via Bitget (0.1%/0.1%) would improve returns.
Paper trades use the same model and signals as the full backtest. The difference: backtests cover years of data across multiple market cycles, while paper trades show month-by-month performance in recent market conditions. Together they provide a complete picture.
Yes. The Omniscius model is retrained bi-weekly, which means paper trade results reflect the exact same model updates that live subscribers receive. This is not a static backtest — it is a living simulation. Learn about Omniscius →
These paper trade results show what our model delivers. Get real-time XLM signals with entry, stop-loss, and take-profit on every trade.
Institutional-grade AI trading signals for crypto traders. Our ensemble models use strict walk-forward validation across multiple Bitcoin halving cycles. No curve-fitting, no hype — just data-driven signals delivered to your Telegram. Methodology and changelog are public on our About page.
Prices delayed up to 5 minutes. Trading and investing involve significant risk of loss. All content on this site is for informational purposes only and does not constitute financial advice. Decisions to buy, sell, or hold are best made with the advice of qualified financial professionals. Past performance does not guarantee future results.
Hypothetical or simulated performance results have inherent limitations. Unlike an actual performance record, simulated results do not represent actual trading. Since the trades have not been executed, the results may have under- or over-compensated for the impact of certain market factors, including lack of liquidity. No representation is being made that any account will or is likely to achieve profit or losses similar to those shown.
Some links on this site are affiliate links. AlgoZilla may earn a commission at no extra cost to you. This does not affect signal quality. Please read our full disclaimer · Terms · Privacy
© 2026 AlgoZilla.io — All rights reserved