Education

What Telonics Is

Telonics is a single systematic portfolio: eight independent edges trading five futures markets, the Nasdaq 100 (NQ), Gold (GC), Dow (YM), S&P 500 (ES), and Crude Oil (CL). It is not a signal service, an education product, or a discretionary trading room.

The portfolio executes fully automatically on your own brokerage or prop-firm account. There are no alerts to follow and no orders to place by hand, the system handles entry, management, and exit for every position.

How It Runs on Your Account

Once your account is connected, the portfolio runs without intervention. Each trade follows the same path, from signal to close:

Signal
Each edge monitors its market for one precisely defined condition. When that condition is met, it generates a trade with a predetermined entry and stop, no discretion involved.
Routing
The order is transmitted through TradersPost, which connects to any broker or prop firm that permits automated, third-party trading. Confirm your firm allows automation before subscribing.
Execution
The trade is placed on your own account. Telonics never holds client capital or broker credentials; you retain full control and can pause or halt at any time.
Management
Each position is managed to its exit, and the entire portfolio is flattened by the daily cut-off, no exposure is carried unattended overnight.

You set a single parameter — risk per trade — and the portfolio sizes every position to your account balance automatically. A larger account trades more contracts; a smaller one, fewer.

How We Build & Validate Edges

Any track record can be assembled after the fact. The relevant question is whether an edge holds up when it is tested honestly, and most do not. Every edge in the portfolio had to clear the same process before it traded a dollar of capital.

Hypothesis
Each edge begins as a specific, testable claim about market behavior, not a set of indicators optimized until the curve looks good.
Walk-forward
Each edge is validated on years of tick-level data, trained on one period and tested on the next, replicating how it would have traded forward in time.
Out-of-sample
The 2025–2026 period was withheld from all development. It serves as a genuine out-of-sample test, eighteen months the models never saw.
Selection
Most candidates fail here. An edge that performs only on the data used to build it is discarded; only those that hold up are retained.
Cost modeling
Every result incorporates realistic commissions and slippage. Nothing depends on idealized fills.

The edges that survive are published in full. Every number, total return, Sharpe ratio, win rate, profit factor, and the complete backtested equity curve, is on our Performance page.

What Out-of-Sample Testing Is

Give a strategy enough freedom and you can always make it look brilliant on the past, you keep adjusting it until it fits the history in front of you. That isn't finding an edge; it's memorizing the answers. The only way to know whether a strategy learned something real is to test it on data it has never seen.

That is out-of-sample testing, and the idea behind it is simple: hide some of the data, and save it for a final test.

You take a block of market history and set it aside, untouched. You build and tune the strategy on all the rest. Then, at the very end, you run it once on the part you saved. If it still works on data it has never seen, that is strong evidence of a real edge. If it falls apart, the strategy was never real, it was just fitting the past.

It's the difference between studying with the answers in front of you and taking a real exam. Only one tells you whether you actually know the material.

Telonics withholds the entire 2025–2026 period, eighteen months, as a true out-of-sample test, and publishes how the portfolio performed on it. A cherry-picked chart, by contrast, is one hundred percent in-sample: it only shows the trades that already worked.

Why Costs & Slippage Matter

Every real trade costs money, and those costs are the difference between a backtest that looks good and one that would actually work. There are two:

Commission is the fee your broker and the exchange charge every time you place a trade. It's a fixed amount per contract, and you pay it twice on every trade, once to get in and once to get out. Small on its own, but it adds up fast across thousands of trades.

Slippage is the gap between the price you wanted and the price you actually got. Markets move constantly, so in the split second between your order being sent and it filling, the price can shift a little against you. Usually a tick or two, but it's real money, and it gets worse when the market is moving fast, around news, or right at the open.

A backtest that ignores these shows profit that would never have existed in a live account. And it matters more than it sounds: plenty of strategies have edges so thin that realistic costs erase them entirely — profitable on paper, negative once you subtract what it actually takes to trade.

A backtest with no costs isn't optimistic, it's fiction.

So we don't skip them. Every Telonics figure is calculated after costs, not before: each simulated trade is charged real commission plus a full tick of slippage on both entry and exit, the same friction you'd hit in a live account. When you see a return anywhere that doesn't mention costs, assume it hasn't survived them.

For funded & prop traders

How Prop Firms Actually Work

A proprietary trading firm gives you access to a large account in exchange for an evaluation fee, typically $100–$600. Pass the evaluation, often called a combine, and the firm funds you with $25,000 to $250,000 or more, with most of the profit paid to the trader (commonly 80–90%). In return, the account runs under a strict rule set: daily loss limits, maximum and trailing drawdown, consistency requirements, and time limits.

The economics are worth understanding. A large share of prop-firm revenue comes from those evaluation fees, and most people who attempt one do not pass. The reason usually is not the firm, it is that most traders arrive with a strategy that has no real edge, and a strict rule set only surfaces that faster. Even those with a workable approach tend to break the behavioral rules under pressure: revenge trading after a loss, oversizing to recover, forcing trades out of impatience.

Where automation fits

This is the case for automating a validated edge: a real, tested edge solves the first problem, and automated execution solves the second. The rules that trip up discretionary traders are behavioral, and automation removes the behavior.

Daily loss limits
The system does not revenge-trade after a losing session. It continues to follow the same rules.
Drawdown limits
Position size is defined mathematically. Exposure cannot escalate after a loss.
Consistency rules
Every session is traded the same way, with no outsized discretionary positions.
Time-based rules
Trades occur only when an edge's conditions are met, never out of impatience.

The portfolio is constructed to respect these constraints: each edge carries a defined, ATR-normalized stop; the portfolio is sized to a fixed fraction of the account; exposure is diversified across sessions and five markets; and all positions are flattened by the daily cut-off. Automation is not a loophole, it is discipline enforced in code.

We run this exact portfolio live on our own prop-firm accounts, the same automation our subscribers use. Our incentive is performance, not selling courses, signals, or a strategy we wouldn't run ourselves.
An honest note on the rules

Automation removes the behavioral errors that fail most evaluations. It does not guarantee that you pass or remain funded. Prop-firm rules — trailing and daily drawdown, consistency, and news restrictions — continue to apply, and a sequence of correlated losing days, slippage, or a technical failure can still breach them. Results vary, and past performance does not predict future results.

Before subscribing, confirm that your prop firm permits fully automated trading executed by a third-party service. Some firms restrict or prohibit it, and compliance with your firm's terms is your responsibility. See our Risk Disclosure.

Reference

Key Terms, Explained

The metrics and concepts we publish, in plain English. If you followed a link to get here, your term is right below.

Edge
A real, repeatable advantage in a market: a pattern that makes money more often than not across a large number of trades, even after costs. It is what separates a genuine strategy from random luck. Most strategies sold online have no edge, they may win for a while by chance, but over enough trades they break even or lose.
Win rate
The percentage of trades that end in a profit. A 56% win rate means 56 of every 100 trades close in the green. On its own it means little, though: a high win rate paired with tiny wins and huge losses still loses money. It only matters alongside the size of the wins versus the losses.
Profit factor
Gross profit divided by gross loss: for every $1 the system loses, how many dollars it makes back. Anything above 1.0 is profitable. A profit factor of 1.36 means the portfolio earns $1.36 for every $1 it gives back.
Sharpe ratio
A measure of return relative to risk: how much reward a strategy produces for each unit of volatility it takes on. Higher is better. Above 2 is excellent, and above 3 is rare even among top hedge funds. It rewards steady, consistent returns and penalizes wild swings.
Backtest (and multi-year backtest)
Running a strategy against years of historical market data to see how it would have performed. A multi-year backtest runs it across many years and many different market conditions. Backtested results are hypothetical, not live trading, and they are only meaningful when realistic trading costs are included.
Walk-forward testing
A stricter form of backtest. You build and tune the strategy on one stretch of history, then test it on the next stretch it has never seen, and repeat, moving forward through time. It mimics how the strategy would actually have traded going forward, instead of being fitted to the entire past at once.
Out-of-sample
A block of history deliberately set aside during development and never used to build the strategy, then used only to test the finished version. It is the real exam: if the strategy holds up on data it has never seen, that is genuine evidence of an edge rather than a strategy curve-fitted to the past.
Noise
Random, meaningless movement in the data. Markets are full of patterns that show up purely by chance, and if you dig long enough you can always find one that fit the past perfectly. Because it was random to begin with, it falls apart the moment you test it on new data. Telling a real, repeatable edge apart from noise is the whole point of honest testing.

A systematic edge, fully automated.

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