Prominent market scholars have delved into the cyclical behavior of stock markets, emphasizing the significance of grasping asset seasonality. In this article, we will explore how to identify seasonal patterns using Python. What is a seasonal pattern? According to José MartÃnez, a seasonal pattern pertains to the market behavior of specific assets within a particular time frame. For instance, commodities like soy, wheat, and oil are influenced by climatic conditions. Do other financial assets respond to seasonal patterns? Yes, seasonal patterns can correlate with regular events such as employment data, stock levels, or consumption. Dimitri Speck has demonstrated that seasonality outperforms other investment strategies. To analyze this, we employ quartiles and a box and whisker plot, statistical tools. Quartiles divide data into four parts, while the box and whisker plot visually displays the median and quartiles. We apply these tools to Corn contract data, uncovering seasonal patterns in returns. For instance, the months of June, July, and September exhibit significant variations. Certain periods, like from July to September and from November to December, yielded positive returns. We also scrutinize the daily variation of Corn in previous years, noting returns tending to stay close to zero. When observing return variation in a specific month, such as June, we discover that investing on Mondays and closing positions on Tuesdays or Wednesdays could generate positive returns. Similarly, for December, trading from Wednesday to Friday might be advantageous. We proceed to explore studies based on heatmaps, which visualize the magnitude of a phenomenon using colors. We observe that buying on Mondays and selling on Thursdays in July could be an effective strategy. Next, we remove data trends to avoid biases and update our study. Although previous patterns become less clear, the potential for increases from November to December still remains. For visual confirmation, we use the statsmodels library to decompose data into trend, seasonal, and residual components. This reveals how closing price, trend, and seasonal components relate. In summary, studies on seasonal patterns in markets offer valuable insights. Understanding these trends can enrich your investment strategies and enable informed decision-making.
%% IT varies, on seasonals\apples frequently drop in SEPT\one of the main harvest months; same with stocks\ETFs. Stock Traders Almanac by Hirsch has some.
There's this guy in a stocks facebook group who obsessively follows lunar cycles and correlates them with S&P etc in painstaking detail. Whatever works I guess, but the charts were hilarious.
Here is a free sites like this one- but your barely skimming the surface. If you are serious about trading get the 'Traders Almanac'. https://insider-week.com/en/seasonal-charts/
this chart suggests buying and holding may be a half decent approach to investing. the trick is the 10 year average part.
There may well be seasonality, but just to be clear you aren't doing any real statistics there, just some basic data visualization. (I recognize you never claimed otherwise.) If you go ahead and calculate p-values and then apply a Bonferroni correction (for example), I'd be surprised if what you have is statistically significant. Now, nobody says you have to have statistical significance in order to place a bet, that's a matter of personal choice - but I imagine some people with less background in quantitative areas will be reading this and think that somehow Mondays in July have been "proven" to exhibit profit potential, but that's just not true. Of course, that doesn't by itself prove that they don't provide profit potential, either. I would suggest that next you include commissions and slippage to see what impact that has on your results. Happy trading.
%% OK; but i never considered business or a business risk a bet. And 100years+ 200/all of it / years of US stock trends helps, however its defined.[IBD+ more data] I dont pay commissions but bid \ask is expense also. Looks like with your[ pic], you never confused wild bison with a pet, but that's just an educated guess LOL
Sure - the term 'bet' is old language I adopted from money management books I was reading in the 90s that extended the literature on gambling to trading. Many of the foundations of probability and statistics were developed beginning in the 17th century in order to analyze games of chance. I still use 'bet' even though I think you're right that it's not the best terminology. Replace 'place a bet' with 'establish a position' or 'make a trade.' More data is always better, but if more data doesn't move the p-value, it doesn't make the case any stronger. In fact, one might say it makes the case weaker because you have more statistical power and yet still couldn't reject the null hypothesis! LOL. I took that pic years ago in northern Canada. No confusion here!