Shaanxi Zhuoyue Digital Technology Consulting Service Co., Ltd.
I am an independent trader and algorithmic trading developer focused primarily on gold, Bitcoin, U.S. futures, and the foreign exchange market.
I did not enter trading because I believed there was a perfect strategy that could win forever. What attracted me was the relationship between price, probability, risk, and human behavior. Instead of trying to predict every candle, I care more about whether a trading system can continue to operate logically across different market cycles, volatility conditions, and periods of consecutive losses.
I have spent a significant amount of time developing strategies, running historical tests, validating parameters, and observing real-market execution. Many systems look impressive in a backtest, but the results can change completely once spreads, slippage, commissions, broker rules, and actual execution conditions are included. For that reason, I prefer to face drawdowns and failures honestly rather than only showing the most attractive equity curve.
My approach to EA development is straightforward: the trading logic must be clear, the risk must be explainable, the backtest must cover a meaningful range of market conditions, and every strategy must survive repeated questioning and revision.
To me, a valuable trading system is not simply the one that earns the most in a short period of time. It is a system that still understands why it is entering, why it is increasing exposure, and when it must stop—even when the market becomes difficult.
I do not promise a loss-free strategy, and I do not present high returns as risk-free. Uncertainty will always be part of the market. What I can do is continue improving the logic, controlling risk exposure, and testing every system against both time and real trading conditions.
Every system published here represents many hours of testing, failure, correction, and revalidation. None of them is perfect, but I take every detail seriously and stand behind the work I share.
Trading is a long game.
I am still learning, and I am still evolving.
I did not enter trading because I believed there was a perfect strategy that could win forever. What attracted me was the relationship between price, probability, risk, and human behavior. Instead of trying to predict every candle, I care more about whether a trading system can continue to operate logically across different market cycles, volatility conditions, and periods of consecutive losses.
I have spent a significant amount of time developing strategies, running historical tests, validating parameters, and observing real-market execution. Many systems look impressive in a backtest, but the results can change completely once spreads, slippage, commissions, broker rules, and actual execution conditions are included. For that reason, I prefer to face drawdowns and failures honestly rather than only showing the most attractive equity curve.
My approach to EA development is straightforward: the trading logic must be clear, the risk must be explainable, the backtest must cover a meaningful range of market conditions, and every strategy must survive repeated questioning and revision.
To me, a valuable trading system is not simply the one that earns the most in a short period of time. It is a system that still understands why it is entering, why it is increasing exposure, and when it must stop—even when the market becomes difficult.
I do not promise a loss-free strategy, and I do not present high returns as risk-free. Uncertainty will always be part of the market. What I can do is continue improving the logic, controlling risk exposure, and testing every system against both time and real trading conditions.
Every system published here represents many hours of testing, failure, correction, and revalidation. None of them is perfect, but I take every detail seriously and stand behind the work I share.
Trading is a long game.
I am still learning, and I am still evolving.
Friends
5
Bo Wen Che
Liuguanyi3X 在原版 Engine A 基础上增加 H9 第二收益引擎。 在当前历史回测基准中，组合净利润相比原版利润提升约 45.57%。最大回撤由 6.945% 上升至 8.713%，增加 1.768 个百分点。Profit / 1% DD 从 919.57 提高至 1,066.96，
策略核心仍由原版 Engine A 执行。H9 主要在 Engine A 进入特定回撤状态后寻找第二收益机会，通过更高周期趋势、EMA结构和短周期回踩确认寻找补充入场；在深度回撤且与主引擎风险重叠时，通过动态风险矩阵调节额外暴露。新版本同时将 H9 与原版资金档位完全同步，以 2500档=1× 为基础，5000档=2×、10000档=4×，其他档位依次同比例增加盈利与风险
Liuguanyi3X integrates the H9 secondary alpha engine into the core Engine A foundation.
Based on current historical backtest benchmarks, combined net profit improved by ~45.57% relative to the legacy model. Maximum Drawdown (Max DD) expanded from 6.945% to 8.713% (+1.768 percentage points), while the Profit / 1% Max DD ratio upgraded from 919.57 to 1,066.96.
Core trade execution continues to be driven by Engine A. H9 operates conditionally, identifying auxiliary entry points when Engine A experiences designated drawdown phases—filtering opportunities via higher-timeframe trends, EMA structure, and lower-timeframe pullback confirmations. Under severe drawdown scenarios with overlapping exposure, a dynamic risk matrix dynamically modulates additional leverage.
Furthermore, H9 risk levels are fully aligned with the baseline account tier system ($2,500 = 1×, $5,000 = 2×, $10,000 = 4×), ensuring strictly proportional sizing for expected returns and drawdown across all capital tiers
策略核心仍由原版 Engine A 执行。H9 主要在 Engine A 进入特定回撤状态后寻找第二收益机会，通过更高周期趋势、EMA结构和短周期回踩确认寻找补充入场；在深度回撤且与主引擎风险重叠时，通过动态风险矩阵调节额外暴露。新版本同时将 H9 与原版资金档位完全同步，以 2500档=1× 为基础，5000档=2×、10000档=4×，其他档位依次同比例增加盈利与风险
Liuguanyi3X integrates the H9 secondary alpha engine into the core Engine A foundation.
Based on current historical backtest benchmarks, combined net profit improved by ~45.57% relative to the legacy model. Maximum Drawdown (Max DD) expanded from 6.945% to 8.713% (+1.768 percentage points), while the Profit / 1% Max DD ratio upgraded from 919.57 to 1,066.96.
Core trade execution continues to be driven by Engine A. H9 operates conditionally, identifying auxiliary entry points when Engine A experiences designated drawdown phases—filtering opportunities via higher-timeframe trends, EMA structure, and lower-timeframe pullback confirmations. Under severe drawdown scenarios with overlapping exposure, a dynamic risk matrix dynamically modulates additional leverage.
Furthermore, H9 risk levels are fully aligned with the baseline account tier system ($2,500 = 1×, $5,000 = 2×, $10,000 = 4×), ensuring strictly proportional sizing for expected returns and drawdown across all capital tiers