There are countless mechanical trading systems for financial trading, yet each has a critical weakness: under certain adverse market conditions, the system fails. This is unsurprising—every such system assumes a predictable, uninterrupted price move against open positions and estimates—often subjectively—the maximum expected retracement. Such assumptions cannot be correct. Price movement, its intensity, speed, and the magnitude of any reversal are inherently unpredictable. Attempts to forecast them have been made repeatedly, but none has delivered consistent, reliable success. Errors occur frequently—and sometimes lead to catastrophic losses for traders.
Technical Analysis — Not Recommended!
Contemporary technical analysis largely consists of rigid rules treated as dogma—designed to predict future price direction. Yet even the choice of indicator used to trigger entries and exits implies skepticism about its reliability.
In fact, changing recommended input parameters or the investment horizon dramatically shifts entry points. Subsequent trading then follows the first (simple) attractor—yielding only local gains, but consistently producing small losses (around 1–2%) across repeated trades. Using the second (cyclical) attractor inevitably leads to averaging, which most traders dismiss outright—not because it’s theoretically unsound, but because they see no objective way to determine the correct position size for the next trade at a given price level. Yet that calculation is entirely feasible—though building a mechanical trading system based on conventional technical analysis makes it nearly impossible to account for all inevitable negative factors.
Classical technical analysis can be summarized in just a few steps: Use trend-following indicators (e.g., moving averages) to identify direction; find a presumed entry point; decide how much capital to risk per trade (accounting for potential loss); set stop-loss and take-profit levels. That’s it—no further active decisions are possible. You simply wait.
If technical analysis worked reliably, everyone with basic arithmetic skills (e.g., multiplication tables) could master it and profit consistently. But that doesn’t happen. Instead, encouraged by early wins, traders often increase risk—and ultimately lose their entire deposit. Thus, modern technical analysis cannot be considered a reliable tool for exchange trading and should not be recommended for practical use.
Our goal isn’t to criticize technical analysis—it’s already been thoroughly discredited by real-world outcomes. To build a truly functional trading system, we must first abandon technical analysis as a discipline, and second, stop trying to predict price direction altogether—recognizing that such attempts are not only futile but dangerous. Luck may produce short-term profits and confidence—but eventual losses are inevitable. It’s only a matter of time.
How Averaging Works
The question arises: Is consistent profit even possible? In their book “How to Buy Stocks,” L. Angel and B. Boyd answer directly: The only way to “beat” the market is through averaging. Let’s clarify what averaging means.
Take the simplest example. Suppose you buy 5 apples at 10 RUB each, spending 50 RUB total. (Prices and product names are illustrative only.) Later, apples drop to 7 RUB each—and you buy another 12, spending 84 RUB. Now you own 17 apples for 134 RUB total—so your average cost per apple is 7.88 RUB. This is averaging. You lowered your effective purchase price from 10 RUB to 7.88 RUB by buying more at a lower price. Expressed algebraically, the formula is:
P(N+n) = (N × PN + n × Pn) / (N + n), where
- P(N+n) = average price per unit for the total quantity;
- PN = initial purchase price;
- N = initial quantity purchased at PN;
- Pn = subsequent purchase price;
- = quantity purchased at Pn.
As shown, this basic formula calculates only the average cost of your total position. Neither N nor n is predetermined—and price itself is an objective, exogenous variable, independent of our calculations. While illustrated with apples, this logic applies directly to trading: replace “apples” with “shares” or “currency units,” and “price per apple” with “asset price.”
“Extra Shares” — The Market’s Gift to Traders
A key insight concerns the quantity to buy (or sell) at each specific price level. Solving this requires linking position size to available capital—i.e., your deposit. Without tying the next trade’s size to your current balance, trading remains highly speculative. So our task is formally defined: calculate , the number of units we can afford to buy at price Pn, given that N (initial position size) is known and finite. (How to determine N is explained below.)
Let D = your deposit. Asset price is at PN. At this level, you buy N units, costing N × PN. Remaining cash is D − N × PN; remaining purchasing power in units is (D − N × PN) / PN.
At PN, your deposit could buy D / PN units—but you bought only N. Note again: N is not yet defined (we’ll address that later). Since you’re long, upward price movement yields profit—so we ignore that scenario. Instead, assume price moves against you—to Pn—where you must buy additional units. But is unknown. We must solve for it.
First, compute your unrealized loss at Pn: N × (PN − Pn). Your remaining cash becomes D − N × PN − N × (PN − Pn) = D − 2 × N × PN + N × Pn. From this amount, you can buy (D − 2 × N × PN + N × Pn) / Pn units at price Pn.
We now have two “remaining capacity” values: at PN, it’s (D − N × PN) / PN; at Pn, it’s (D − 2 × N × PN + N × Pn) / Pn. Assume N is small enough that capacity at Pn exceeds capacity at PN. Then the difference—((D − 2 × N × PN + N × Pn) / Pn) − ((D − N × PN) / PN)—represents the “extra” units you can acquire as price falls against you. Simplifying algebraically yields:
((D − 2 × N × PN) × (PN − Pn)) / (PN × Pn)
This expression quantifies the excess units gained due to downward price movement—i.e., you buy more units with less money. This surplus is the market’s “gift” to the trader. It’s the “extra” or “excess” position built passively as price moves against your initial trade.
We’ve now solved for at Pn—and can use it safely. However, using the full surplus is unnecessarily aggressive. Instead, use half of it:
n = ((D − 2 × N × PN) × (PN − Pn)) / (2 × PN × Pn)
This is the averaging algorithm. To apply it, simply embed this formula in Excel.
Using this formula is only the first step. Next, we must define N—the initial position size. We’ll cover that shortly, as the current formula isn’t directly applicable to the FOREX market. We’ll adapt it with minor adjustments to suit currency trading—necessary because FOREX presents both unique constraints and advantages. For example, stop orders require margin only upon execution—unlike stock exchanges, where stop orders immediately tie up margin from the main deposit, limiting placement flexibility. We’ll explore these distinctions in detail later.
FAQ
What is averaging in trading?
Averaging (or ‘averaging down’) is a strategy where traders add to a losing position to lower their average entry price—hoping price will reverse and turn the trade profitable.
Is averaging profitable on Forex?
It can work in ranging markets with strict risk controls, but carries high risk of margin call during strong trends—especially without proper position sizing.
What’s the biggest risk of averaging?
Exponential drawdown: each added position increases exposure and margin usage, making the trade vulnerable to liquidation if price continues against you.
