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Forex Strength Investing

Greetings Traders!

I'm Pablo, a trader in the stock market, forex, and indices for over 12 years. As an investor and trader, I often use the eToro platform. While I rarely use MT4, I realized that I have coding skills and know how to code an expert advisor. Recognizing this, I decided to create an expert advisor that can trade in the market automatically, especially in forex. I am aware that many traders struggle with market analysis, and a significant number incur losses every day.

Trading Signal:

High Risk : Click Here

Rental and Pricing:

You can rent my EA for $130 per month. However, this is a limited-time offer, and I will remove the rental option soon. The current price of the Orion is $1,000 USD. Soon, the price will increase to $2,600 USD, so take advantage of the current pricing while it lasts.

Forex Strength Investing utilizes various indicators and a semi-ai approach for automated trading. Additionally, Forex Investing identifies patterns such as harmonic patterns and support/resistance levels to determine trade execution. Forex Investing employs the martingale strategy but analyzes markets to determine when and if it should be used, ensuring confirmation based on indicators. Moreover, Orion also features automm to protect your funds.

Recommended timeframe: 
M15

Supported Currency Pairs:

  • AUDCAD, AUDNZD, NZDCAD

Minimum Capital:

  • $3,000 to $10,000
  • Note: With higher capital increases profit potential, while lower capital limits profit potential.

Easy to Set-Up Settings:
  • The EA must be attached to M15 chart, AUDCAD.

Choose one of these:

  • 1000 High Risk or 2000 Low Risk
  • For automm: 1000
  • Lotsize only: 0.01
  • If automm equals 1000 and Lot only equals 0.01, then automm is running.

Note:

  • Use a Low Spread Account like Exness.
  • Do not involve other EAs.
  • Recommended leverage: 1:500
  • Restart MT4 three times.
  • The expected drawdown according to backtests (high risk settings) is no more than 45%.
  • The expected drawdown according to backtests (low risk settings) is no more than  25%.

Simplified Installation and Setup:

The EA settings require minimal input parameters because they are built into the code. This design ensures that you do not have to struggle with complicated installation and setup processes.

Successful Trades:

Once you have completed the setup, it is crucial to be patient and wait for trades to occur. Success with this EA requires being patient.

Could you please add me to my business Facebook account? My FB link: fb.com/business-profile


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EA has high-performance live track records of different set files: XAU Risky Vol XAU Balanced Vol XAU Balanced MT5 version can be found here Golden Pickaxe is a mean-reversion grid trading system that uses machine learning technology to place high-profit potential trades on the Gold market. It uses real market inefficiencies to its advantage to have an edge over the market. The EA has 5 predefined set files, which are essentially 5 different trading systems on gold . You may choose the def
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Dmitriq Evgenoeviz Ko
"Aurus Gold" is a program that can automatically analyze and trade on the foreign exchange market (Forex) without human intervention. This innovative tool for decisions about buying or selling currency pairs. The main task of Aurus Gold is to maximize profits and minimize risks for investors. It is able to work around the clock, based on predetermined parameters and trading rules. The main benefits of using a currency robot include: 1. Trade automation: The ability to carry out transact
AbuTrader
Murodillo Eshkuvvatov
Seulement 10 exemplaires à partir de 800$ Bon moment de la journée --- en bref, nous avons créé un robot forex 100% automatisé appelé AbuTrader (pas besoin de confirmation humaine du tout) il peut gagner de l'argent pendant que vous dormez ou vivez une vie PROP FIRM spécial Set FILE pour réussir le test ou gérer de vrais comptes réussis toutes les limites confortables * fonctionne toutes les paires majeures et mineures * Période - sélection automatique avec le comportement P
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Exler Consulting GmbH
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IMPORTANT: Please contact me after buying or renting for the correct key paramter.  Before you buy a night scalper please be aware of the risks involved: Past performance is no guarantee of future profitability (the EA could also make losses).  The backtests shown (e.g. in screenshots) are highly optimized to find the best parameters but therefore results cannot be transferred to live trading. Any mean reversion can get caught on the wrong side of a fast movements due to unexpected news or flas
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Nadiya Mirosh
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Kirill Borovskii
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Plus de l'auteur
Trading Signal: We currently don't have a signal for this EA. We’re waiting to have three buyers before adding a signal for it. For now, it’s just a demo. Rental and Pricing: You can rent my EA for $130 per month. However, this is a limited-time offer, and I will remove the rental option soon. The current price of the Orion is $1,300 USD. Soon, the price will increase to $3,600 USD, so take advantage of the current pricing while it lasts. Orion Intelligence System Overview: The Orion Intelligenc
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Version 3.5 2024.07.31
What's new:

double RSI_m5 = iRSI(Symbol(), 5, RSI_Period, PRICE_CLOSE, 0);
double RSI_m15 = iRSI(Symbol(), 15, RSI_Period, PRICE_CLOSE, 0);
double RSI_m30 = iRSI(Symbol(), 30, RSI_Period, PRICE_CLOSE, 0);

RSI_m5 = NormalizeDouble(RSI_m5, 2);
RSI_m15 = NormalizeDouble(RSI_m15, 2);
RSI_m30 = NormalizeDouble(RSI_m30, 2);

bool isOverbought = RSI_m5 > 70 && RSI_m15 > 70 && RSI_m30 > 70;
bool isOversold = RSI_m5 < 30 && RSI_m15 < 30 && RSI_m30 < 30;

if (isOverbought) {
Global_22_a_168_ko[Para_0_in].bo_6 = true;
} else if (isOversold) {
Global_22_a_168_ko[Para_0_in].bo_5 = true;
} else {
Global_22_a_168_ko[Para_0_in].bo_5 = false;
Global_22_a_168_ko[Para_0_in].bo_6 = false;
}

Global_22_a_168_ko[Para_0_in].bo_5 &= Global_22_a_168_ko[Para_0_in].do_9 < Local_49_do && Local_49_do > 0.0;
Global_22_a_168_ko[Para_0_in].bo_6 &= Global_22_a_168_ko[Para_0_in].do_9 > Local_48_do && Local_48_do > 0.0;
tmp_in_10 = 0;
Version 3.0 2024.07.25
What's new:

double RSI_m5 = iRSI(Symbol(), 5, RSI_Period, PRICE_CLOSE, 0);
double RSI_m15 = iRSI(Symbol(), 15, RSI_Period, PRICE_CLOSE, 0);
double RSI_m30 = iRSI(Symbol(), 30, RSI_Period, PRICE_CLOSE, 0);

RSI_m5 = NormalizeDouble(RSI_m5, 2);
RSI_m15 = NormalizeDouble(RSI_m15, 2);
RSI_m30 = NormalizeDouble(RSI_m30, 2);

bool isOverbought = RSI_m5 > 70 && RSI_m15 > 70 && RSI_m30 > 70;
bool isOversold = RSI_m5 < 30 && RSI_m15 < 30 && RSI_m30 < 30;

if (isOverbought) {
Global_22_a_168_ko[Para_0_in].bo_6 = true;
} else if (isOversold) {
Global_22_a_168_ko[Para_0_in].bo_5 = true;
} else {
Global_22_a_168_ko[Para_0_in].bo_5 = false;
Global_22_a_168_ko[Para_0_in].bo_6 = false;
}

Global_22_a_168_ko[Para_0_in].bo_5 &= Global_22_a_168_ko[Para_0_in].do_9 < Local_49_do && Local_49_do > 0.0;
Global_22_a_168_ko[Para_0_in].bo_6 &= Global_22_a_168_ko[Para_0_in].do_9 > Local_48_do && Local_48_do > 0.0;
tmp_in_10 = 0;
Version 2.6 2024.07.01
What's new:

double RSI_m5 = iRSI(Symbol(), 5, RSI_Period, PRICE_CLOSE, 0);
double RSI_m15 = iRSI(Symbol(), 15, RSI_Period, PRICE_CLOSE, 0);
double RSI_m30 = iRSI(Symbol(), 30, RSI_Period, PRICE_CLOSE, 0);

RSI_m5 = NormalizeDouble(RSI_m5, 2);
RSI_m15 = NormalizeDouble(RSI_m15, 2);
RSI_m30 = NormalizeDouble(RSI_m30, 2);

bool isOverbought = RSI_m5 > 70 && RSI_m15 > 70 && RSI_m30 > 70;
bool isOversold = RSI_m5 < 30 && RSI_m15 < 30 && RSI_m30 < 30;

if (isOverbought) {
Global_22_a_168_ko[Para_0_in].bo_6 = true;
} else if (isOversold) {
Global_22_a_168_ko[Para_0_in].bo_5 = true;
} else {
Global_22_a_168_ko[Para_0_in].bo_5 = false;
Global_22_a_168_ko[Para_0_in].bo_6 = false;
}

Global_22_a_168_ko[Para_0_in].bo_5 &= Global_22_a_168_ko[Para_0_in].do_9 < Local_49_do && Local_49_do > 0.0;
Global_22_a_168_ko[Para_0_in].bo_6 &= Global_22_a_168_ko[Para_0_in].do_9 > Local_48_do && Local_48_do > 0.0;
tmp_in_10 = 0;
Version 2.5 2024.06.05
What's new:

double RSI_m5 = iRSI(Symbol(), 5, RSI_Period, PRICE_CLOSE, 0);
double RSI_m15 = iRSI(Symbol(), 15, RSI_Period, PRICE_CLOSE, 0);
double RSI_m30 = iRSI(Symbol(), 30, RSI_Period, PRICE_CLOSE, 0);

RSI_m5 = NormalizeDouble(RSI_m5, 2);
RSI_m15 = NormalizeDouble(RSI_m15, 2);
RSI_m30 = NormalizeDouble(RSI_m30, 2);

bool isOverbought = RSI_m5 > 70 && RSI_m15 > 70 && RSI_m30 > 70;
bool isOversold = RSI_m5 < 30 && RSI_m15 < 30 && RSI_m30 < 30;

if (isOverbought) {
Global_22_a_168_ko[Para_0_in].bo_6 = true;
} else if (isOversold) {
Global_22_a_168_ko[Para_0_in].bo_5 = true;
} else {
Global_22_a_168_ko[Para_0_in].bo_5 = false;
Global_22_a_168_ko[Para_0_in].bo_6 = false;
}

Global_22_a_168_ko[Para_0_in].bo_5 &= Global_22_a_168_ko[Para_0_in].do_9 < Local_49_do && Local_49_do > 0.0;
Global_22_a_168_ko[Para_0_in].bo_6 &= Global_22_a_168_ko[Para_0_in].do_9 > Local_48_do && Local_48_do > 0.0;
tmp_in_10 = 0;
Version 2.4 2024.03.10
double RSI_m5 = iRSI(Symbol(), 5, RSI_Period, PRICE_CLOSE, 0);
double RSI_m15 = iRSI(Symbol(), 15, RSI_Period, PRICE_CLOSE, 0);
double RSI_m30 = iRSI(Symbol(), 30, RSI_Period, PRICE_CLOSE, 0);

RSI_m5 = NormalizeDouble(RSI_m5, 2);
RSI_m15 = NormalizeDouble(RSI_m15, 2);
RSI_m30 = NormalizeDouble(RSI_m30, 2);

bool isOverbought = RSI_m5 > 70 && RSI_m15 > 70 && RSI_m30 > 70;
bool isOversold = RSI_m5 < 30 && RSI_m15 < 30 && RSI_m30 < 30;

if (isOverbought) {
Global_22_a_168_ko[Para_0_in].bo_6 = true;
} else if (isOversold) {
Global_22_a_168_ko[Para_0_in].bo_5 = true;
} else {
Global_22_a_168_ko[Para_0_in].bo_5 = false;
Global_22_a_168_ko[Para_0_in].bo_6 = false;
}

Global_22_a_168_ko[Para_0_in].bo_5 &= Global_22_a_168_ko[Para_0_in].do_9 < Local_49_do && Local_49_do > 0.0;
Global_22_a_168_ko[Para_0_in].bo_6 &= Global_22_a_168_ko[Para_0_in].do_9 > Local_48_do && Local_48_do > 0.0;
tmp_in_10 = 0;
Version 2.3 2024.03.03
Version 2.3 Fixed

-No errors and Warnings
Version 2.2 2024.02.17
Version 2.2 Fixed

-No errors and Warnings
Version 2.1 2024.02.09
Version 2.1 Fixed and is Mandatory.
Version 2.0 2024.01.31
Version 2.0 Fixed and is Mandatory.
Version 1.9 2024.01.31
Version 1.9 Fixed and is Mandatory.
Version 1.8 2024.01.31
Version 1.8 Fixed and is Mandatory.
Version 1.7 2024.01.28
Version 1.7 Fixed and is Mandatory.
Version 1.6 2024.01.27
Version 1.6 Fixed and is Mandatory.
Version 1.5 2024.01.26
Version 1.5 Fixed and is Mandatory.
Version 1.4 2024.01.25
Version 1.4 Fixed
Version 1.3 2024.01.24
Version 1.3 Fixed
Version 1.2 2024.01.09
Version 1.2 Fixed
Version 1.1 2023.12.30
Version 1.1 Fixed