AlgoBot is a trading bot that specializes in technical analysis. It has been developed with more than 17,000 lines of code, alongside AI and machine learning algorithms. This means AlgoBot operates independently, so no human input is required.

It also means AlgoBot learns and improves indefinitely, constantly adjusting and adapting to new market conditions. AlgoBot doesn’t ‘think’ like a human trader. It can evaluate millions of potential market outcomes as every second passes.
Not just for forex but crypto assets, stocks, indices, and commodities. Trading decisions are determined by hundreds of in-built technical and economic indicators, such as:

• Moving Averages (MA)
• Relative Strength Index (RSI)
• Moving Average Convergence Divergence (MACD)
• Bollinger Bands
• Stochastic Oscillator
• Fibonacci Retracements
• Average True Range (ATR)
• Ichimoku Cloud
• Parabolic SAR
• On-Balance Volume (OBV)
• Accumulation/Distribution Line
• Commodity Channel Index (CCI)
• Momentum Indicator
• Williams %R
• Volume Weighted Average Price (VWAP)
• Keltner Channels.

Now let’s consider an example of how AlgoBot uses technical indicators to set up a risk-averse trade.
First, AlgoBot leverages the Moving Averages (MA) indicator on the BTC/USD trading pair. It discovers that the 50-day MA has just crossed the 200-day MA, known as the ‘Golden Cross’. This highlights that BTC/USD has just entered bullish territory, meaning a buy order is appropriate.

1. AlgoBot also leverages the Relative Strength Index (RSI) for confirmation of the signal. The RSI has a reading of 50, meaning it’s neither overbought nor oversold. While this doesn’t strengthen the Golden Cross signal, it reduces the risk of a sudden market downturn.
2. AlgoBot also leverages the Moving Average Convergence Divergence (MACD). It discovers that the MACD line has crossed above the signal line, which is another bullish indicator.
3. AlgoBot determines that the best entry point is just after the Golden Cross zone. It deploys its stop-loss order below the most recent price swing. This ensures the trade is closed with a minimal loss if the identified triggers are false readings.
4. AlgoBot leverages the Fibonacci Retracement levels to deploy its take-profit order. This is in line with BTC/USD’s historical resistance levels.

AlgoBot’s specific stop-loss and take-profit levels are also determined by the user’s risk setting. This covers conservative, balanced, and aggressive.

A conservative setting takes the least amount of risk. This means the risk-reward ratio for each position is modest. An aggressive risk setting will target the highest gains. This means it will have wider stop-loss and take-profit targets. A balanced risk setting sits in between conservative and aggressive.
The user can change their risk setting at any time. An effective strategy is to deploy AlgoBot in demo mode with your chosen trading platform. This will provide a clear understanding of how each risk setting works.

AlgoBot trades in all market conditions, irrespective of whether prices are bullish or bearish. AlgoBot is also effective in sideways markets.

This enables AlgoBot to trade long and short positions around the clock. Put simply, users can profit from rising and falling markets without needing to lift a finger.

AlgoBot leverages Good-till-Canceled orders when entering a position. This ensures the bot enters the market at the most effective pricing point. AlgoBot leverages technical indicators to achieve this goal.

For instance, AlgoBot might use the Fibonacci Retracement to assess support and resistance levels. Alternatively, it might utilize the Parabolic SAR to evaluate potential trend reversal points.
Either way, the Good-till-Canceled order will remain pending until:

• AlgoBot’s target entry price is triggered by the markets
• AlgoBot cancels the order

Stop-loss orders are crucial when trading financial instruments. They’re the most important risk management tool available to traders. Therefore, AlgoBot deploys stop-losses on all positions.

The stop-loss percentage will depend on the identified trading opportunity and the user’s risk setting. AlgoBot makes an informed decision based on technical readings.

For instance, AlgoBot might leverage the Average True Range (ATR) indicator to assess volatility.

• Let’s say the ATR reading on EUR/USD is currently 0.02 (200 pips).
• AlgoBot calculates the stop-loss order at 1.5 times the ATR distance from the entry price.
• This would be below the entry price on a long position. And vice versa on a short position.
• If the trade doesn’t go to plan, it will be closed as soon as the stop-loss price is triggered.

Take-profit orders are also an important part of AlgoBot’s risk management strategy. This order type ensures that profit targets are locked in as soon as they’re triggered.

• Let’s suppose AlgoBot is going short on Brent Crude Oil, which is currently priced at $80 per barrel.
• The 50-day MA is sat at $75, suggesting a potential revision area.
• Therefore, the take-profit target might be set just above the MA reading at $75.50.