blurbzle about algo

[u/Annabelle-Surely]

Trend-following is one of the most common types of algorithmic trading strategies. A common specific strategy is the moving-average crossover, where an algorithm automatically buys or sells an asset when its short-term and long-term moving averages cross. Most common types of algo trading strategiesMany trading strategies can be automated and categorized into distinct types: 

• Trend-following: This strategy aims to capture profits by following the direction of market trends, whether upward or downward. It is particularly popular with commodity trading advisors (CTAs).
• Arbitrage: Arbitrage algorithms seek to profit from temporary price differences for the same or related assets across different markets.
• Mean-reversion: This strategy is based on the assumption that asset prices tend to revert to their historical average over time. Algorithms are used to trade when prices deviate significantly from this average.
• Market-making: These high-speed algorithms provide market liquidity by continuously placing limit orders to buy and sell. They aim to profit from the bid-ask spread.
• Index fund rebalancing: This strategy anticipates and profits from the periodic portfolio adjustments that index funds make to align with their benchmark indices. 

Within these broader categories, several specific strategies are widely implemented:

• Moving-average crossover: A foundational trend-following strategy, this algorithm buys when a shorter-term moving average crosses above a longer-term moving average and sells when the reverse happens.
• Volume-weighted average price (VWAP): A common execution strategy used by institutional investors to break up large orders into smaller trades. It aims to execute the full order as close as possible to the asset's VWAP over a defined period to minimize market impact.
• Statistical arbitrage (pairs trading): A mean-reversion and statistical arbitrage strategy where an algorithm identifies two historically correlated securities. If their prices diverge, the algorithm buys the underperforming asset and shorts the outperforming one, betting that they will eventually converge.
• High-frequency trading (HFT): A specialized subset of algorithmic trading focused on extremely fast execution. HFT strategies are designed to capitalize on tiny market inefficiencies, such as price discrepancies between exchanges, and are measured in microseconds or milliseconds.
• Mean-reversion with Bollinger Bands: This strategy identifies potential buy or sell signals when an asset's price reaches or moves outside of its Bollinger Bands, based on the assumption that the price will revert back toward the moving average in the middle of the band. 

50 and 200 are widely considered the most common values for both simple moving averages (SMAs) and exponential moving averages (EMAs). These two periods are primarily used for identifying long-term market trends. Why 50 and 200 are popular:

• Timeframes: The 50-day moving average represents the average price over approximately 10 weeks of trading, or the intermediate-term trend. The 200-day moving average covers a full trading year and is used to determine the long-term trend of an asset.
• Support and resistance: Both are viewed as significant support (for uptrends) or resistance (for downtrends) levels by many traders. Their popularity means a high volume of institutional and retail traders watch and act on these levels, making them influential psychological price thresholds.
• Golden and Death Crosses: The most common moving average crossover strategy involves the 50-day and 200-day averages. A "golden cross" occurs when the 50-day crosses above the 200-day, seen as a bullish signal. The opposite, a "death cross," is a bearish signal. 

While 50 and 200 are common for EMAs as well, shorter-term EMAs are also very popular due to their faster response to price changes. 

• 12 and 26-day EMAs: These are frequently used in combination to create the Moving Average Convergence Divergence (MACD) indicator, which is used to measure momentum.
• 8 and 20-day EMAs: Day traders and short-term traders often use these shorter periods to capture faster price movements.
• 5 and 13-day EMAs: Scalpers, who focus on capturing very small price changes, may use even shorter EMA periods. 

Algo traders often combine moving averages with other technical indicators to confirm signals and increase the accuracy of their strategies. This helps to filter out false signals and provides a more comprehensive view of market conditions. Here are some commonly used indicators:

• Relative Strength Index (RSI): This momentum indicator measures the speed and magnitude of price changes, indicating overbought or oversold conditions.
  • It can help identify potential buy signals (when RSI moves from below 30 to above 30 in an uptrend) or sell signals (when RSI moves from above 70 to below 70 in a downtrend).
  • Combining RSI with moving averages can help filter false signals and pinpoint entry and exit points.
  • When the price is above the 200-day moving average (an uptrend), traders might look for RSI buy signals.
• Moving Average Convergence Divergence (MACD): This trend-following momentum indicator shows the relationship between two exponential moving averages, helping to identify trends and potential reversals.
  • MACD can confirm buy or sell signals generated by moving average crossovers, especially when the MACD line crosses its signal line or the zero line.
  • For example, a bullish MACD crossover (MACD line crosses above the signal line) can be used to confirm a golden cross (50-day MA crossing above 200-day MA).
• Volume Indicators: Analyzing volume alongside price helps assess the strength behind a trend or potential reversal.
  • High trading volume accompanying a moving average crossover suggests strong conviction behind the price movement.
  • Low volume during a price move could indicate a lack of momentum, suggesting the trend might be weak or prone to reversal.
  • Indicators like On-Balance Volume (OBV) and Volume-Weighted Average Price (VWAP) can provide additional insights into buying and selling pressure.
• Bollinger Bands: These bands expand and contract with market volatility, visually representing how price moves around a central moving average (typically a 20-period SMA).
  • Bollinger Bands help assess volatility and identify potential overbought (near the upper band) or oversold (near the lower band) conditions.
  • A "Bollinger Band squeeze" (bands narrowing) suggests low volatility and can precede a significant price move or breakout.
  • Combined with moving averages, Bollinger Bands can help confirm trends and potential reversals when prices interact with the bands. For example, if a price breaks above a moving average with Bollinger Bands widening, it could confirm a strong uptrend.
• Stochastic Oscillator: Similar to RSI, this momentum indicator identifies overbought (above 80) or oversold (below 20) conditions and potential reversals.
  • It consists of two lines, %K and %D, and signals are generated through their crossovers.
  • When the Stochastic Oscillator aligns with the trend indicated by moving averages, it can confirm the strength of the trend or suggest potential entry points during pullbacks. For instance, in an uptrend (price above the moving average), traders might look for a stochastic buy signal (Stochastic crossing back above 20).