Shenjingwangluo qq513439419
Reliability
4 weeks (since 2018)
0
0 USD
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Growth

Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
YTD
Total:

Balance

Equity

Drawdown

  • Equity
  • Drawdown
Trades:
220
Profit Trades:
161 (73.18%)
Loss Trades:
59 (26.82%)
Best trade:
11.09 USD
Worst trade:
-14.28 USD
Gross Profit:
203.87 USD (27533 pips)
Gross Loss:
-120.30 USD (14508 pips)
Maximum consecutive wins:
21 (14.52 USD)
Maximal consecutive profit:
30.20 USD (14)
Sharpe Ratio:
0.17
Trading activity:
83.02%
Max deposit load:
6.52%
Latest trade:
59 minutes ago
Trades per week:
73
Avg holding time:
8 hours
Recovery Factor:
3.65
Long Trades:
113 (51.36%)
Short Trades:
107 (48.64%)
Profit Factor:
1.69
Expected Payoff:
0.38 USD
Average Profit:
1.27 USD
Average Loss:
-2.04 USD
Maximum consecutive losses:
6 (-12.97 USD)
Maximal consecutive loss:
-14.28 USD (1)
Monthly growth:
41.78%
Algo trading:
100%

Distribution

Symbol Deals Sell Buy
GBPCAD 34
GBPAUD 34
USDJPY 25
EURCAD 24
AUDCAD 19
GBPUSD 19
NZDCAD 16
GBPCHF 16
GBPNZD 14
CHFJPY 13
AUDNZD 6
10203040
10203040
10203040
Symbol Gross Profit, USD Loss, USD Profit, USD
GBPCAD 16
GBPAUD 60
USDJPY -6
EURCAD -1
AUDCAD 6
GBPUSD 14
NZDCAD -5
GBPCHF -1
GBPNZD -8
CHFJPY 3
AUDNZD 4
20406080
20406080
20406080
Symbol Gross Profit, pips Loss, pips Profit, pips
GBPCAD 2.5K
GBPAUD 8.7K
USDJPY -426
EURCAD 164
AUDCAD 1K
GBPUSD 1.6K
NZDCAD -504
GBPCHF -10
GBPNZD -1K
CHFJPY 449
AUDNZD 630
2K4K6K8K10K
2K4K6K8K10K
2K4K6K8K10K
Best trade:
11.09 USD
Maximum consecutive wins:
21 (14.52 USD)
Maximal consecutive profit:
30.20 USD (14)
Worst trade:
-14.28 USD
Maximum consecutive losses:
6 (-12.97 USD)
Maximal consecutive loss:
-14.28 USD (1)
Drawdown by balance:
Absolute:
0.42 USD
Maximal:
22.87 USD (7.85%)
Relative drawdown:
By Balance:
9.90% (21.93 USD)
By Equity:
6.05% (16.33 USD)

MFE and MAE Distribution Point Graphs

Maximum profit (MFE) and maximum loss (MAE) values are recorded for each open order during its lifetime. These parameters additionally characterize each closed order using the values of the maximum unrealized potential and maximum permitted risk. MFE/Profit and MAE/Profit distribution graphs display each order as a point with received profit/loss value plotted along the X-axis, while maximum displayed values of potential profit (MFE) and potential loss (MAE) are plotted along the Y-axis.

No data
No data

Place your cursor over parameters/graph captions to see the best and worst trading series. Find out more about MAE and MFE distributions in the article Mathematics in Trading: How to Estimate Trade Results.

The average slippage based on execution statistics on real accounts of various brokers is specified in pips. It depends on the difference between the provider's quotes from "ICMarkets-Live11" and the subscriber's quotes, as well as on order execution delays. Lower values mean better quality of copying.

ICMarkets-Live12
0.00 × 1
FXGlory-Real Server
0.00 × 3
ICMarkets-Live06
1.00 × 3
Tickmill-Live02
1.00 × 1
XMUK-Real 15
1.07 × 41
ICMarkets-Live11
1.51 × 86
ICMarkets-Live10
2.03 × 30
AUSForex-Live
2.13 × 8
ICMarkets-Live05
2.40 × 5
ICMarkets-Live02
2.80 × 41
ICMarkets-Live01
2.94 × 36
ICMarkets-Live03
3.22 × 18
Alpari-Pro.ECN
3.50 × 8
ICMarkets-Live07
4.11 × 9
ICMarkets-Live04
4.14 × 7
ICMarkets-Live14
4.58 × 12
ICMarkets-Live09
5.00 × 2
Pepperstone-Edge04
5.33 × 150
FBS-Real-9
7.14 × 14
FXOpen-ECN Live Server
9.20 × 5
HFMarketsSV-Live Server 4
17.00 × 1
ForexTimeFXTM-ECN
18.34 × 119
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What is a neural network?
Artificial neural networks (ANNs) are examples of information processing inspired by biological nervous systems such as the brain, process information, and the like. The key element of this paradigm is the novel structure of the information processing system. It consists of a large number of highly interconnected processing elements (neurons) to coordinate work to solve specific problems. ANN is like a person, learning by example. ANNs are configured into specific applications through learning processes, such as pattern recognition or data classification. Learning biological systems involves adjusting the synaptic connections that exist between neurons. The same is true for artificial neural networks.

 

Why do you use neural networks for trading?
Neural networks can derive meaning from complex or inaccurate data that can be used to extract patterns and detect overly complex trends that cannot be discovered by humans or other computer technologies. A well-trained neural network can be thought of as an "expert" in the category of information it is analyzed. This expert can then be used to provide predictions for a given new situation and answer the "hypothesis" question.

Other advantages include:

Adaptive learning: The ability to learn how to complete a task based on given training data or initial experience.
Self-organizing: Artificial neural networks can create representations of their own organizations or information received during their learning.
Real-time operation: ANN calculations can be performed in parallel.
 how to work

Quantitative and qualitative forecasting methods help managers set business goals. Business forecasts can be based on historical data patterns used to predict future market behavior. The time series prediction method is a data analysis tool that can measure historical data points - for example, using a line chart - to predict future conditions and events. The goal of the time series approach is to identify meaningful features in the data that can be used to state future outcomes.

In order to generate the depth and invariant features of one-stop foreign exchange price forecasting, a deep learning-based forecasting scheme is used to provide a deep learning framework for financial time series, which integrates the architecture of stacked automatic encoders and long-term long-term memory. . The framework involves three phases:

Data preprocessing uses wavelet transform to decompose the time series of foreign exchange prices to eliminate noise;
An application of a stacked autoencoder with a deep architecture trained in an unsupervised manner; and
Delay using long-term short-term memory to generate a one-step advance output.
method of prediction

In particular, this program consists of three parts. The first part is the training part, which is used to train the model and update the model parameters. The second part is the verification part. It uses it to adjust hyperparameters and get the best model settings. The last one is the test part, we use the optimal model to predict the data. In the training section, we used the data from the past decade to train the model.

 

Expert Advisor recommended configuration
Very easy to use. No more configuration is required, then the batch is adjusted to the desired value. Under the time frame and paired with EA:

No reviews
2018.12.08 01:47
This is a newly opened account, and the trading results may be of random nature
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Signal
Price
Growth
Subscribers
Funds
Balance
Weeks
Expert Advisors
Trades
Win %
Activity
PF
Expected Payoff
Drawdown
Leverage
3000
USD
42%
0
0
USD
284
USD
4
100%
220
73%
83%
1.69
0.38
USD
10%
1:500
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