增长自 2024
-3%
交易:
40
盈利交易:
18 (45.00%)
亏损交易:
22 (55.00%)
最好交易:
67.84 USD
最差交易:
-32.08 USD
毛利:
173.52 USD
(4 014 pips)
毛利亏损:
-228.41 USD
(6 775 pips)
最大连续赢利:
4 (18.27 USD)
最大连续盈利:
67.84 USD (1)
夏普比率:
-0.09
交易活动:
92.39%
最大入金加载:
3.16%
最近交易:
1 几天前
每周交易:
21
平均持有时间:
15 小时
采收率:
-0.42
长期交易:
14 (35.00%)
短期交易:
26 (65.00%)
利润因子:
0.76
预期回报:
-1.37 USD
平均利润:
9.64 USD
平均损失:
-10.38 USD
最大连续失误:
4 (-43.00 USD)
最大连续亏损:
-45.66 USD (3)
每月增长:
-2.74%
算法交易:
100%
结余跌幅:
绝对:
131.94 USD
最大值:
131.94 USD (6.60%)
相对跌幅:
结余:
6.60% (131.94 USD)
净值:
1.91% (37.04 USD)
分配
交易品种 | 交易 | Sell | Buy | |
---|---|---|---|---|
EURUSD_i | 10 | |||
AUDUSD_i | 10 | |||
GBPUSD_i | 8 | |||
USDCAD_i | 7 | |||
USDCHF_i | 5 | |||
2
4
6
8
10
|
2
4
6
8
10
|
2
4
6
8
10
|
交易品种 | 毛利, USD | 损失, USD | 利润, USD | |
---|---|---|---|---|
EURUSD_i | 34 | |||
AUDUSD_i | -39 | |||
GBPUSD_i | -36 | |||
USDCAD_i | -3 | |||
USDCHF_i | -10 | |||
25
50
75
100
125
150
175
200
|
25
50
75
100
125
150
175
200
|
25
50
75
100
125
150
175
200
|
交易品种 | 毛利, pips | 损失, pips | 利润, pips | |
---|---|---|---|---|
EURUSD_i | 120 | |||
AUDUSD_i | -1.1K | |||
GBPUSD_i | -1.3K | |||
USDCAD_i | -153 | |||
USDCHF_i | -403 | |||
250
500
750
1K
1.3K
1.5K
1.8K
2K
2.3K
2.5K
2.8K
3K
|
250
500
750
1K
1.3K
1.5K
1.8K
2K
2.3K
2.5K
2.8K
3K
|
250
500
750
1K
1.3K
1.5K
1.8K
2K
2.3K
2.5K
2.8K
3K
|
- 入金加载
- 提取
最好交易:
+67.84
USD
最差交易:
-32
USD
最大连续赢利:
1
最大连续失误:
3
最大连续盈利:
+18.27
USD
最大连续亏损:
-43.00
USD
基于有关不同交易商真实账户的执行统计的平均滑移点按点数指定。它取决于 Weltrade-Real 提供商以及订阅者之间不同的报价,以及订单执行的延迟。值越低意味着复制的质量越高。
无数据
This fully automated signal broadcasts the trading process of our innovative trading bot, which was created using neural network learning technology on large-scale market data. The built-in artificial intelligence mathematical model searches for the potential impulse of each subsequent market bar and uses the emerging divergence and convergence patterns between predictive indicators and price to form highly accurate reversal points for opening trading positions.
To solve the task of predicting the bar impulse, we used a Recurrent Neural Network (RNN) with a complex and modern architecture that combines an LSTM layer for analyzing dependencies in time series, BatchNormalization and Dropout for stabilizing learning and preventing overfitting, as well as fully connected layers for extracting higher-level features from the data. Training such a model enables it to capture complex temporal dependencies in historical trading instrument data and make predictions based on these dependencies.
The trading strategy incorporates an adaptive risk management system that dynamically adjusts to the current market volatility, structure, and conditions. It takes into account both short-term and long-term market trends to accurately assess risks at every stage of trading. Unlike many other strategies, our bot does not use position averaging, which allows for risk minimization in the event of unfavorable developments. A strict drawdown control ensures capital protection, effectively preventing significant losses even in force majeure situations, such as sudden market swings or unexpected economic events. This well-thought-out risk management system not only preserves but also grows capital under any market conditions.
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