Many traders long for yesteryear as markets are perceived to become more difficult to trade over time. However, I really do think now may be the best time to be a retail trader ever. Read on to learn learn why the futures and equities landscape has never look better for the retail trader.
First for futures traders, we have the e-micros like micro ES, NQ, and YM (MES, MNQ, MYM). It has been a true game changer because if you are an active day trader, now you can work on your craft in live markets risking just $50 a day (or even less). The e-micros also mean major benefits for system developers because it is possible to deploy futures systems to the live markets with minimal risk and get real data on how they perform.
As for equity traders, we now have multiple high quality brokers offering commission free trading. Of course, one still has to meet the PDT, which is a hurdle– but it could be a real boon for scalpers or certain types of algo strategies.
On the platform side, there are more strong platforms then in years past too. Tradestation now with free data, free platform, etc. could literally represent thousands in savings compared to yesteryear.
On the developing front, I received an email from The Small Exchange that they have been approved as a Designated Contract Market (DCM) with the U.S. Commodity Futures Trading Commission. This could be big if the markets are as tight as the micros, if more markets are offered, and/or the exchange fees and cost to trade is considerably less. It is something worth watching.
Trading may not be any easier but it is certainly less expensive for serious traders to pursue their craft then in years past. If you are a serious sim trader or stuck in a futures tryout treadmill hell or tired of dealing with fake prop firms live access to trade the markets has never been better or cheaper.
The author is passionate about markets. He has developed top ranked futures strategies. His core focus is (1) applying machine learning and developing systematic strategies, and (2) solving the toughest problems of discretionary trading by applying quantitative tools, machine learning, and performance discipline. You can contact the author at email@example.com.
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