I remember when I started trading intraday, I lost 30% of my capital just because I was trying to average the price and thought stop loss was a useless term.
Failing to see the bigger picture of things. -- and instead get lost and scared of crabs and spiders along the way Having a certain degree of logic and reasoning -- as well as intuition, skill, experience and luck.
Some people have a death wish. It is often subconscious but you can see it in their actions that they hope to fail. I have never understood the impulse to average down. Unless the averager wants to fail for some reason. Only you can explain why you did this to yourself. Only losers average losers. -- Paul Tutor Jones Also regarding stoploss: You need some exit strategy. It doesn't have to be a stoploss but it better be something real. Cut Losses Short. Stop trading until you not only get it, you must believe it and practice it automatically.
To believe his own lies. To subdue luck with reason. To subdue reason with emotion. .... To mistake business for leisure.
Without well defined conjecture, there really is nothing for us to be mistaken about and, hence, nothing for us to learn.
What are the biggest mistakes a trader should avoid in stock trading? Thinking (fooling their self that) they know something RN
I was going to say "failure to use stops", but you already figured that one. There are 4 possibilities on a trade. Small gain, small loss, big gain, big loss. The only one you can control is the prevention of "big loss". And if you don't do that, the odds against you go waaaayyyy up.
Trade with a longer term mindset and holding period ( invest ). Short term movements (one hour / day / week action ) are "random"; driven by rumor, headline reaction, supply and demand flow ( "funds" big block purchases and sales ), etc. As our brains are constant predictive machines / generative mechanisms, if you are using "visual" cues based off on "subjective" interpretation ( trendlines, support levels, candlestick, Fibonacci, Elliot wave, chart patterns, divergences, etc. ) then your brain is trying to construct a "representation" from random data that has already "happened" and hence tries to generate a prediction. As with games of chance in gaming establishments, we tend to bypass an empirically driven, probabilites based framework ( and we are barred from using a computer at the blackjack table in order to calculate a probabilities ) and rely on our vagus nerve structure ( gut instinct ) driven by the "go" circuit ( dopamine activated ). The key to statistical success and "deep" geometric compounding, is in building investment models based on statistically significant quantitatively * based variables keyed off of "long term" trends ( multi months, years ). Long term trends relating to the general equity market, reflect the assimilation and integration of macro information ( expected earnings, Federal monetary flows, fiscal decisions, underlying economic strength/ contraction, etc. ) that is pertinent to trend "persistence" and higher incidence of positively skewed statistical outcomes (ie. odds for positive outcomes are much greater in using long trends for forecasting / asset allocation decision making and thus decisions operate from the left cortical structures ( logic driven ) instead of the limbic areas of the ventral stiatum ( impulsive / emotion driven )). A component that one can use put an advantage on one's side towards long term maximization of asset growth is the selection of the stock universe(s) in which to invest. Academic /empirical studies have shown that ( the buy and hold ) of small cap value has produced the highest decile alpha of all stock universes**. Mid cap growth has been promising over the past 30 years. Prior to the 21st century, it was difficult to invest in small cap value as a whole. With the advent of ETFs, it is available in low expense offerings. Same with mid cap growth. Investing in these areas within a Roth IRA or other tax deferred account, provides further advantages. * quantitative = one can empirically apply the method via mathematical representation onto a robust data sample and generate repeatable significant results and output in a probabilistic framework ** https://docs.google.com/document/d/1kToqLWLISRk4n4YnSzv1hT5kBN54l5CvhwGgDwJKPJI/edit?usp=sharing