"Temporal_Volatility_Alpha_forPropFirm" ๋กœ๊ณ 
cBot
๋ฒ„์ „ 1.0, Jan 2026
Windows, Mac, Mobile, Web
4.0
๋ฆฌ๋ทฐ: 2
1
๊ตฌ๋งค
2
์†์ต๋น„
8%
์ตœ๋Œ€ ๋‚™ํญ
"Temporal_Volatility_Alpha_forPropFirm" ์—…๋กœ๋“œ๋œ ์ด๋ฏธ์ง€
"Temporal_Volatility_Alpha_forPropFirm" ์—…๋กœ๋“œ๋œ ์ด๋ฏธ์ง€
"Temporal_Volatility_Alpha_forPropFirm" ์—…๋กœ๋“œ๋œ ์ด๋ฏธ์ง€
"Temporal_Volatility_Alpha_forPropFirm" ์—…๋กœ๋“œ๋œ ์ด๋ฏธ์ง€
"Temporal_Volatility_Alpha_forPropFirm" ์—…๋กœ๋“œ๋œ ์ด๋ฏธ์ง€
"Temporal_Volatility_Alpha_forPropFirm" ์—…๋กœ๋“œ๋œ ์ด๋ฏธ์ง€
"Temporal_Volatility_Alpha_forPropFirm" ์—…๋กœ๋“œ๋œ ์ด๋ฏธ์ง€
"Temporal_Volatility_Alpha_forPropFirm" ์—…๋กœ๋“œ๋œ ์ด๋ฏธ์ง€
"Temporal_Volatility_Alpha_forPropFirm" ์—…๋กœ๋“œ๋œ ์ด๋ฏธ์ง€
10M
๊ฑฐ๋ž˜๋Ÿ‰
10.64K
ํ• ์ˆ˜์ต
20
ํŒ๋งค
1.03K
๋ฌด๋ฃŒ ์„ค์น˜

First Five copies will be sold at 44.

later will increase to 78. Developed in Dec 2025

1. Executive Summary

Temporal Volatility Alpha is a quantitative mean-reversion algorithm designed to capture short-term inefficiencies in the EURUSD pair. The strategy operates on the core premise that price deviations beyond statistical norms (defined by volatility envelopes) present high-probability reversal opportunities, provided they occur during specific high-liquidity time windows.

2. Operational Logic & Methodology

The algorithm utilizes a multi-layered filtering process to isolate high-quality entries:

  • Temporal Gating (Time Filters): Trading is strictly confined to high-volume market overlaps (e.g., London/New York sessions). This "time-segmentation" filters out low-liquidity noise and unpredictable spread widening during off-hours.
  • Volatility Reversion: Entries are triggered based on standard deviation extremes (24-period Bollinger Bands, 2 Deviation). The system identifies overbought/oversold conditions where price is statistically likely to revert to the mean.
  • Momentum Confirmation: An RSI (Relative Strength Index) filter prevents trading against strong impulsive trends, ensuring entries are taken only when momentum is waning.

3. Risk Management Architecture

The system prioritizes capital preservation through a rigid risk framework:

  • Positive Expectancy: The strategy utilizes a fixed 1:2 Risk-to-Reward Ratio (Stop Loss: ~25 pips / Take Profit: ~50 pips). This ensures the algorithm remains profitable even with a win rate as low as 35%.
  • Active Trade Defense: A Volatility-Adjusted Trailing Stop is activated immediately after the position moves 9 pips into profit. This "Breakeven Defense" mechanism rapidly eliminates risk, securing "free trades" and protecting floating equity.

4. Performance Metrics (Challenge Phase)

During the evaluation phase spanning 7 months, the strategy demonstrated exceptional statistical robustness:

  • Net ROI: The strategy achieved a total Net ROI of 73%, significantly outperforming the standard profit targets required for funding.
  • Profit Factor: The algorithm maintained a high Profit Factor of 3, demonstrating that for every $1 of realized risk, the system generated $4 in gross profit.
  • Win Rate & Consistency: The system sustained a 69% Win Rate (56 winning deals out of 81). When paired with the strategy's fixed 1:2 Risk-to-Reward ratio, this high strike rate produced a highly stable and parabolic equity curve with minimal stagnation.

5. Advanced Risk Ratios

  • Sharpe Ratio (> 3): Indicates an "Excellent" risk-adjusted return, confirming the strategy generates high profits with extremely low volatility.
  • Calmar Ratio (> 5): With an annualized pace of ~120% and a maximum drawdown estimated below 5%, this ratio proves the strategy is highly resistant to deep losses.
  • Sortino Ratio (> 4): Confirming that "downside volatility" is virtually non-existent; the risk of a significant losing streak is statistically insignificant.

The strategy has successfully pass prop firm challenge.

ํŠธ๋ ˆ์ด๋”ฉ ํ”„๋กœํ•„
4.0
๋ฆฌ๋ทฐ: 2
5
50 %
4
0 %
3
50 %
2
0 %
1
0 %
๊ณ ๊ฐ ๋ฆฌ๋ทฐ
March 26, 2026
So far, optimization and execution are very promising. Low-frequency trades combined with a lot of patience give this strategy a real edge, which I am willing to test long-term. After a couple of weeks, only one trade has fired, but it was a successful one. So far, I am happy with this bot.
March 9, 2026
The strategy presents a clean mean-reversion concept using Bollinger Bands, RSI filtering and time-segmented trading windows. However the long-term backtest shows negative performance and optimization logs reveal extremely unrealistic stop-loss values, suggesting a potential parameter overflow or optimization bug. The strategy concept is sound but the current implementation appears unstable.
Indices
EURUSD
RSI
Bollinger
ํŠธ๋ ˆ์ด๋”ฉ ๋ด‡, ์ง€ํ‘œ, ํ”Œ๋Ÿฌ๊ทธ์ธ ๋“ฑ cTrader Store์—์„œ ์ œ๊ณต๋˜๋Š” ์ƒํ’ˆ์€ ์ œ3์ž ๊ฐœ๋ฐœ์ž์— ์˜ํ•ด ์ œ๊ณต๋˜๋ฉฐ, ์ด๋Š” ๋‹จ์ˆœํžˆ ์ •๋ณด ๋ฐ ๊ธฐ์ˆ ์  ์ ‘๊ทผ์„ ๋ชฉ์ ์œผ๋กœ ์ œ๊ณต๋œ ๊ฒƒ์ž…๋‹ˆ๋‹ค. cTrader Store๋Š” ์ค‘๊ฐœ์ธ์ด ์•„๋‹ˆ๋ฉฐ, ํˆฌ์ž ์กฐ์–ธ, ๊ฐœ์ธ๋ณ„ ์ถ”์ฒœ ๋˜๋Š” ํ–ฅํ›„ ์„ฑ๊ณผ์— ๋Œ€ํ•œ ์–ด๋– ํ•œ ๋ณด์žฅ๋„ ์ œ๊ณตํ•˜์ง€ ์•Š์Šต๋‹ˆ๋‹ค.

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ํ• ์ˆ˜์ต
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๋ฌด๋ฃŒ ์„ค์น˜