Shenjingwangluo qq513439419
Fiabilidad
4 semanas (since 2018)
0
0 USD
Autorícese o regístrese para ver la estadística detallada

Incremento

ene.
feb.
mar.
abr.
may.
jun.
jul.
ago.
sep.
oct.
nov.
dic.
año
Total:

Balance

Equidad

Reducción

  • Equidad
  • Reducción
Total de Trades:
238
Transacciones Rentables:
173 (72.68%)
Transacciones Irrentables:
65 (27.31%)
Mejor transacción:
11.09 USD
Peor transacción:
-14.28 USD
Beneficio Bruto:
218.79 USD (29 480 pips)
Pérdidas Brutas:
-136.36 USD (16 693 pips)
Máximo de ganancias consecutivas:
21 (14.52 USD)
Beneficio máximo consecutivo:
30.20 USD (14)
Ratio de Sharpe:
0.16
Actividad comercial:
88.37%
Carga máxima del depósito:
7.38%
Último trade:
2 días
Trades a la semana:
66
Tiempo medio de espera:
8 horas
Factor de Recuperación:
3.60
Transacciones Largas:
120 (50.42%)
Transacciones Cortas:
118 (49.58%)
Factor de Beneficio:
1.60
Beneficio Esperado:
0.35 USD
Beneficio medio:
1.26 USD
Pérdidas medias:
-2.10 USD
Máximo de pérdidas consecutivas:
6 (-12.97 USD)
Pérdidas máximas consecutivas:
-14.28 USD (1)
Crecimiento al mes:
41.22%
Trading algorítmico:
100%

Distribución

Símbolo Transacciones Sell Buy
GBPCAD 37
GBPAUD 36
EURCAD 26
USDJPY 25
AUDCAD 20
GBPUSD 20
NZDCAD 18
GBPCHF 17
GBPNZD 15
CHFJPY 15
AUDNZD 9
10203040
10203040
10203040
Símbolo Beneficio Bruto, USD Loss, USD Beneficio, USD
GBPCAD 23
GBPAUD 55
EURCAD 2
USDJPY -6
AUDCAD 7
GBPUSD 17
NZDCAD -11
GBPCHF 1
GBPNZD -11
CHFJPY 4
AUDNZD 3
20406080
20406080
20406080
Símbolo Beneficio Bruto, pips Loss, pips Beneficio, pips
GBPCAD 3.4K
GBPAUD 8K
EURCAD 460
USDJPY -426
AUDCAD 1.1K
GBPUSD 1.8K
NZDCAD -1.3K
GBPCHF 161
GBPNZD -1.5K
CHFJPY 599
AUDNZD 534
2.5K5K7.5K10K13K15K18K20K
2.5K5K7.5K10K13K15K18K20K
2.5K5K7.5K10K13K15K18K20K
Mejor transacción:
11.09 USD
Máximo de ganancias consecutivas:
21 (14.52 USD)
Beneficio máximo consecutivo:
30.20 USD (14)
Peor transacción:
-14.28 USD
Máximo de pérdidas consecutivas:
6 (-12.97 USD)
Pérdidas máximas consecutivas:
-14.28 USD (1)
Reducción de balance:
Absoluto:
0.42 USD
Máxima:
22.87 USD (7.85%)
Reducción relativa:
De balance:
9.90% (21.93 USD)
De fondos:
8.86% (26.04 USD)

Gráficos punteados de distribución MFE y MAE

Durante la vida de cada orden abierta se registran los valores del beneficio máximo (MFE) y pérdida máxima (MAE). Estos índices caracterizan adicionalmente cada orden cerrada con los valores del potencial máximo no realizado y el riesgo máximo cometido. En los gráficos de distribución MFE/Profit y MAE/Profit a cada orden le corresponde un punto donde por la horizontal se da el valor del beneficio/pérdida obtenido/a, y por la vertical se dan los valores del máximo beneficio potencial (MFE) y la máxima pérdida potencial (MAE).

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Sitúe el cursor sobre los índices/leyendas de los gráficos para ver las mejores y las peores series de trading. Puede encontrar más detalles sobre las distribuciones MAE y MFE en el artículo Las matemáticas en el trading. Evaluación de los resultados de las transacciones comerciales.

El deslizamiento medio a base de la estadística de ejecución en las cuentas reales de diferentes corredores se indica en puntos. Depende de la diferencia de las cotizaciones del proveedor de "ICMarkets-Live11" y del suscriptor, así como del retardo en ejecutar las órdenes. Cuanto menos sea este valor, mejor será la calidad del copiado.

FXGlory-Real Server
0.00 × 3
ICMarkets-Live12
0.00 × 1
ICMarkets-Live10
0.90 × 30
ICMarkets-Live06
1.00 × 3
Tickmill-Live02
1.00 × 1
XMUK-Real 15
1.07 × 41
ICMarkets-Live11
1.39 × 92
ICMarkets-Live05
2.00 × 6
AUSForex-Live
2.13 × 8
ICMarkets-Live02
2.67 × 43
ICMarkets-Live03
2.90 × 20
ICMarkets-Live01
2.94 × 36
Tickmill-Live
3.00 × 1
Alpari-Pro.ECN
3.11 × 9
ICMarkets-Live14
3.44 × 16
ICMarkets-Live07
4.11 × 9
ICMarkets-Live04
4.14 × 7
FXOpen-ECN Live Server
4.93 × 15
ICMarkets-Live09
5.00 × 2
Pepperstone-Edge04
5.33 × 150
FBS-Real-9
7.14 × 14
HFMarketsSV-Live Server 4
17.00 × 1
ForexTimeFXTM-ECN
18.34 × 119
Autorícese o regístrese para ver la estadística detallada
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:

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2018.12.08 01:47
This is a newly opened account, and the trading results may be of random nature
Autorícese o regístrese para ver la estadística detallada
Señal
Precio
Incremento
Suscriptores
Fondos
Balance
Semanas
Robots comerciales
Trades
Rentables
Actividad
PF
Beneficio Esperado
Reducción
Apalancamiento
3000
USD
41%
0
0
USD
282
USD
4
100%
238
72%
88%
1.60
0.35
USD
10%
1:500
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