Extending the test or training period far into the past is not always a solution. Log in or Join Our Community. That's extremely similar to the seasonal pattern displayed by the new-crop corn market average high of June 18 , only adjusted a couple weeks later, just like the critical reproductive stage of an American soybean plant is typically a couple weeks later than the critical reproductive stage of an American corn plant.
Soybean Market Seasonality: Unraveled
The fundamental justification for seasonal highs is already getting spread out across three countries, each with different planting, blooming, and harvesting schedules, and that's without even addressing the broad range of planting and harvesting dates that can take place within each country.
Just as the U. So I was surprised when the data from CBOT-traded soybean futures contracts actually seemed more heavily influenced by North American soybean timeframes than by South American timeframes. In years of normal abundance, like or , the November futures contract tended to reach its highest level of the year in June June 11, , and June 30, That's extremely similar to the seasonal pattern displayed by the new-crop corn market average high of June 18 , only adjusted a couple weeks later, just like the critical reproductive stage of an American soybean plant is typically a couple weeks later than the critical reproductive stage of an American corn plant.
In years when the soybean crops were short, like or , the average date of the November soybean contract's annual high arrived on Oct. That's uncanny because that's the exact same date as the average high for the December corn contract during short crop years. I will note that the annual highs I identified for November soybean contracts were, as a whole, more spread out and less reliable than the clusters of corn highs I saw.
The seasonal pattern for normal-abundance years -- and may turn out to be a year of normally abundant soybean production -- showed spurts of risk premium being priced in during the summer, then generally lower prices through the harvest. But, there were a few outliers when the contract's annual high hit during South American planting: I don't claim there's much statistical reliability in these 15 years of data, arbitrarily sorted out into categories of production abundance.
In addition to that, I make all the same disclaimers I made about the corn seasonality study: This analysis was a study of historical data, not a prediction of future events. No one has independently verified my calculations, and I'm not recommending that anyone should buy or sell commodity futures or commodity options or cash commodities based on this analysis alone. Most importantly, just knowing the historical highs for certain categories of years doesn't solve the eternal problem of figuring out what kind of weather year it will be.
Nothing will ever solve that problem. There's no way to truly know, in February, how large the soybean crop on either continent will be, or whether it will feel like normal abundance or a shortage, or a sudden crush of supply.
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Hint the bid bots don't get all the curation money, the author of the post does. Ok thanks you very much. And you will not necessarily notice this in the backtest. The more data you use for testing or training your strategy, the less bias will affect the test result and the more accurate will be the training.
Even shorter when you must put aside some part for out-of-sample tests. Extending the test or training period far into the past is not always a solution. The markets of the s or s were very different from today, so their price data can cause misleading results.
But there is little information about how to get to such a system in the first place. The described strategies often seem to have appeared out of thin air. Does a trading system require some sort of epiphany? Or is there a systematic approach to developing it?
The first part deals with the two main methods of strategy development, with market hypotheses and with a Swiss Franc case study. All tests produced impressive results. So you started it live. Situations are all too familiar to any algo trader. Carry on in cold blood, or pull the brakes in panic? Several reasons can cause a strategy to lose money right from the start.
It can be already expired since the market inefficiency disappeared. Or the system is worthless and the test falsified by some bias that survived all reality checks. In this article I propose an algorithm for deciding very early whether or not to abandon a system in such a situation. You already have an idea to be converted to an algorithm. You do not know to read or write code.
So you hire a contract coder. Just start the script and wait for the money to roll in. Clients often ask for strategies that trade on very short time frames.
Others have heard of High Frequency Trading: The Zorro developers had been pestered for years until they finally implemented tick histories and millisecond time frames. Or has short term algo trading indeed some quantifiable advantages? An experiment for looking into that matter produced a surprising result. For performing our financial hacking experiments and for earning the financial fruits of our labor we need some software machinery for research, testing, training, and live trading financial algorithms.
No existing software platform today is really up to all those tasks. So you have no choice but to put together your system from different software packages. Fortunately, two are normally sufficient. We will now repeat our experiment with the trend trading strategies, but this time with trades filtered by the Market Meanness Index.
So they all would probably fail in real trading in spite of their great results in the backtest. This time we hope that the MMI improves most systems by filtering out trades in non-trending market situations. It can this way prevent losses by false signals of trend indicators.