Algorithmic Trading Strategy
Understanding Algorithmic Trading Strategies
Algorithmic trading strategies involve a series of stages, akin to the process of building machine learning models. First, one defines a strategy in a computationally testable format. Next, preliminary tests or back tests are conducted. Once these tests are completed, the strategy undergoes optimization. Finally, the strategy's performance and resilience are assessed.
The effectiveness of trading strategies is primarily determined through back testing. Here, one uses historical data to simulate trades that would have taken place in the past based on the devised strategy. This simulation offers insights into the potential success of the strategy. It serves as a foundation from which one can refine and adapt the strategy before engaging with live markets.
A common underlying assumption in this approach is that past performance might be indicative of future results. That is, a strategy successful in the past may likely succeed in the future, while an underperforming one may continue to falter.
1. Date Range:
- The dataset covers from May 27, 2014 to May 22, 2020, providing us with approximately 6 years of data to analyze.
2. Price Variation:
- In May 2014, the opening price of the stock was around $88, and by May 2020, this price has risen to over $313. This denotes substantial growth in Apple's stock value over a span of 6 years.
3. Trading Volume:
- Over time, the trading volume seems to fluctuate. For instance, on May 30, 2014, there was a particularly high trading volume of 141,005,200. In contrast, on May 22, 2020, the volume stood at 20,430,600. Specific events or product launches might have influenced trading volumes.
4. Volatility:
- There are days when the stock experienced higher volatility. For example, on May 30, 2014, the stock opened at $91.14 and closed at $90.43, but it reached a peak of $92.02 and a low of $89.84 during the day. Such variability within a single day indicates that there were events or news that impacted investors' perception of the stock's value.
5. Adjusted Closing Price:
- The adjusted closing price takes into account factors like dividends and stock splits. For most of the dates provided, the closing price and the adjusted closing price appear to be similar, suggesting there weren't many corporate events that adjusted the price during this period.
6. General Trend:
- Although only a small sample of the data was provided, it can be inferred that Apple has experienced steady growth over these 6 years, with some fluctuations in between.
- 30-day Simple Moving Average (SMA30):
- The first 29 values are NaN because there aren't enough previous data points to calculate an average for those days. Starting from the 30th day, an average of the previous 30 days is calculated. In the latest data provided (up to row 1509), the SMA30 is approximately 295.03. This indicates that on average, over the last 30 days up to that point, the adjusted closing price was around 295.03.
- 100-day Simple Moving Average (SMA100):
- The first 99 values are NaN for the same reason as before. Starting from the 100th day, an average of the previous 100 days is calculated. In the latest data provided (up to row 1509), the SMA100 is approximately 290.92. This means that on average, over the last 100 days up to that point, the adjusted closing price was around 290.92.
Analysis and Observations:
- Trend and Support/Resistance:
- SMAs are widely used in stock technical analysis to identify trends. If a stock's adjusted closing price is above its SMA100 or SMA30, it might indicate the stock is in an upward trend. Conversely, if it's below, it might suggest a downward trend. Additionally, SMAs often act as support or resistance levels. If a stock drops and then bounces off its SMA, that SMA might be acting as support. If a stock rises and then reverses at its SMA, it might be acting as resistance.
- Comparison between SMA30 and SMA100:
- Comparing these two SMAs can provide buy or sell signals. For instance: Golden Cross: When the SMA30 crosses above the SMA100, it's traditionally a buy signal.
- Death Cross: When the SMA30 crosses below the SMA100, it's traditionally a sell signal. In your data, the SMA30 (295.03) is slightly above the SMA100 (290.92) on the last provided row. If the SMA30 keeps rising while the SMA100 stays the same or declines, it could reinforce a bullish trend signal.
- Volatility:
- The SMA30, being a short-term average, will be more responsive to recent price changes and thus will be more volatile. On the other hand, the SMA100, being a long-term average, will be less responsive to daily changes and will show a smoother trend.With these two moving averages, you can gain a clearer insight into the stock's general direction and potential decision points in terms of trading or investment strategies. Of course, it's crucial to combine this information with other tools and analysis before making financial decisions.
Understanding the Signals:
- The presence of NaN values in the Buy Signal Price and Sell Signal Price columns for the early rows signifies the absence of signals, which is expected because SMA values are not available for the first few entries due to the rolling window's nature.
- When the Buy Signal Price has a value (not NaN), it means that on that particular day, the conditions for a buy signal were met. Similarly, values in Sell Signal Price represent days when selling is advisable based on the SMA crossover strategy.
Chart Analysis:
- The plotted chart gives a clear visualization of the relationship between Apple's stock price, SMA30, SMA100, and the generated buy/sell signals.
- By observing the crossing points of the SMA lines and the buy/sell markers, one can understand and validate the timing of the buy/sell signals.
- The title, labels, and legends help interpret the visualization clearly.
Strategy Significance:
- The crossover strategy, based on moving averages, is a trend-following strategy. It aims to capitalize on momentum shifts in the stock price.
- When the short-term moving average (SMA30) surpasses the long-term moving average (SMA100), it suggests that the stock is gaining upward momentum, indicating a potential buy. Conversely, when the SMA30 drops below the SMA100, it implies a possible decline in the stock's momentum, signaling a potential sell.