Supply, demand and liquidity - uses and automation

How to make use of supply, demand and liquidity zones for better entries and exits

Supply and demand in daytrading is about finding areas where sellers or buyers are in control. These form pockets of liquidity where one side overpowers the other, slightly or massively, causing price to slide or leap to the next area. This price discovery process takes place from the smallest nanosecond scale, to tick charts, minute charts up to daily and monthly. This is often referred to as fractal but I’ll leave that definition to the mathematicians who have stricter definitions of self-same-ism.

Identifying these areas turns out to be useful at each stage of the trading process. High return entries are possible, or high R (for risk reward ratio), as it is otherwise known, exits can be planned, as well as scaling in or out points throughout the trade.

What’s more, this applies to any market auction, instrument or asset, as buyers and sellers continuously get trapped, take profit, change direction and ebb and flow until some new consensus is reached and off it goes again. Macro to micro, the structure plays out.

Initially I’ve focused on marking these out manually, having worked with a big liquidity afficionado, eventually developing a tool with hotkeys to draw the zones and levels instantly without visiting the menus. This is available for sale if you’d like to try it yourself. It should save you much friction in your manual charting.

Next I plan to highlight the areas I would draw manually automatically. I’ll seek out the liquidity areas that follow accumulation of supply and demand, and where we expect price to retrace to, for a tight entry when it returns, filling the holes in the price ladder left by the rush of buying or selling on the strongest initial leg of the move. See Chris Lori’s videos on order flow for more on this, among others. It is a bizarre yet highly logical feature of every market. It’s not quite clockwork, due to random perturbations, but it happens all the time and can be relied upon to guide trade narratives.

The end goal being to automate the detection of reversals within these areas.

Different approaches to finding liquidity zones for supply and demand automatically

To automate this mapping process, there are a number of indicators at our disposal:

Zigzag - highs, lows, HH, HL, easily mapped out, see TraderSumo for more examples. This lags due to needing to wait for next peak to form before previous is confirmed, but is generally solid and makes it clear.

Donchian - new highs, new lows, easy to automate reading of these levels. Easier than Zigzag, with no lag.

Breakout - mark out area, i.e. the Asia session highs and lows, and breaks of this range are the new direction for the day. Rarely as straightforward, but that’s the idea.

Zline - for every turn including FTR (failure to return). This is more complex, but basically when price extends and then returns, the zero line is where traders were zeroed out or their positions liquidated by a fake move. The idea being to take the other side of this move on further confirmation. This happens to be useful for liquidity trading.

I already have code for most of this but need to unify these approaches by mixing, matching, testing. The major initial issue is that peaks are forming the zones, where it’s actually the lower breakouts that are typically the liquidity areas revisited, particularly as the price slowly slides in that direction on higher timeframes, it comes back into each peak to test it and “reload” on, say, shorts if the peak keeps forming lower highs. So perhaps a % retrace approach could be useful, where the bottom 20% of the peak is highlighted, or the area price broke out from last X bars range. Something along these lines. I have a method or two I won’t publish yet that could well solve this issue, but more testing is required.

Automatic charting pros and cons

So of course there will be compromises to make. Too many or too few zones drawn, no discretion just fixed rules, requiring discretion at every alert, or missed opportunities constantly. But you get a sense of confidence and peace in only selecting optimal areas, if your testing proves valid over 1000s of highlighted areas. So accepting there will be edge cases and compromise comes with the territory.

The other major issue is not knowing whether the liquidity will lead to a break or bounce, which sets you back at square one, with no understanding of the auction and price discovery process. So we select for certain situations that are more likely to offer good entries in various situations, and try to distinguish between the two. Is price likely to bounce quickly and far in a trend, or in a range? How do we approximate trend vs range? See the post for some more thoughts on that subject. But you can see it is a nuanced area even from a cursory look.

One other point worth mentioning is it’s comparison with Volume Profile. A histogram of volume at each price. I typically use VP over a 10 day range, where shelves or ranging areas are highlighted cleanly. This approximates liquidity areas but not the most recent, and not the area where liquidity evaporated and the other side took over. If you used VP daily and assumed a revisit of the POC, that could be interesting, but there are many levels and nodes formed and again distinguishing which is most relevant to the current microstructure is not as well served as zones not yet breached by price drawn on the chart.

How to use this automated zone drawer?

The plan is to use its signals as the basis for a semi automatic trading algo/bot/EA which takes other entry signals within these areas, and offers them to the trader for approval, whereon it enters and manages the trade until completion. Still discretion based, just with much less constant analysis required. A few checks a day for key areas being mapped automatically and then waiting for entry signals at those prices.

I have plans for a diverse range of entries covering each market regime, so passing prop challenges with this system should be much less stressful and time consuming, provided it can make enough sense of the market to assist the trader. And prop firms continue to allow automated assistance. It should be in their interests really, as a profit share in their traders’ various original approaches to the market.

This is the challenge. I hope you’ll come along with me for the ride.

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