| name | coinflip-strategy |
| description | Evaluate a deterministic coin-flip trade signal with a fixed reward-to-risk, and report its true expectancy. Use as the null hypothesis a real strategy must beat, to answer 'is my reward-to-risk enough on its own?', or to compute the win rate an indicator must reach to break even after spread. Advisory only: never size or place trades. It has no edge by construction. |
Coinflip: the null hypothesis
This skill picks a direction at random and attaches a fixed reward-to-risk. It exists to be
beaten, not traded. Its purpose is to make a specific, checkable claim visible: a good
reward-to-risk ratio does not create an edge.
Use the bundled CLI as the source of truth. Never estimate these levels from a chart.
The claim, and why it holds
If price is a driftless random walk, the probability of touching a +rr × R target before a
-1R stop is exactly 1 / (1 + rr). At 1:1.5 that is 40%. Widening the target makes the win
rarer in exactly the proportion that makes it more valuable, so expectancy before costs is
zero at every reward-to-risk ratio. This is the optional stopping theorem. It is a property
of barriers on a martingale, not a claim about any particular market.
Costs break the symmetry, and always in one direction. Substituting the probability above into
the expectancy formula collapses it to EV(R) = -cost_r: the expected loss per trade equals
exactly the cost paid per trade, at every reward-to-risk ratio. Read
references/expectancy.md before telling a user anything about this strategy's viability.
Run it
- Read
references/input-format.md when preparing bar data.
- Configure each symbol's timezone and session windows. Nothing here has verified that any
particular session or weekday is best — those are the user's priors, not measurements.
- Gather completed bars at the configured interval, with a read-only market-data tool.
- Write one input JSON file with an explicit
as_of timestamp.
- Run:
coinflip-evaluate --config config.yaml --input session-bars.json --pretty
- Always report the
expectancy block alongside any signal. It is not optional detail.
Reporting the direction without expected_value_r and edge_required misrepresents what
this skill is.
Interpret states
stand_down: the local weekday is excluded by config.none: as_of falls outside every configured session window.invalidated: too few completed bars to measure volatility, or a degenerate stop.signal_long / signal_short: the coin's call, with entry, stop, target, and expectancy.
The coin is seeded from the symbol and its session open, so it is one flip per symbol per
session. Re-evaluating five minutes later re-reads the same coin rather than tossing a new
one. Same bars and same as_of always produce the same answer, exactly as for every other
strategy here.
Rank a watchlist
Never rank on spread. Rank ascending on expectancy.cost_r.
Because EV(R) = -cost_r, the only thing that matters is spread relative to the stop, and
the stop scales with volatility. A tight spread on a quiet instrument is worse than a wide
spread on a lively one. Concretely, at stop_atr_multiple: 0.5:
| spread | ATR(M5) | stop | cost_r | EV/trade |
|---|
| 0.2 | 1.0 | 0.50 | 0.4000 | −0.4000R |
| 1.0 | 9.0 | 4.50 | 0.2222 | −0.2222R |
The first symbol's spread is five times tighter, and it is more than twice as expensive to
trade. Screening a watchlist by spread alone selects exactly the wrong instruments.
The volatility and expectancy blocks are emitted in every state, including none and
stand_down, so a watchlist can be ranked before any session opens. To build one:
- Pull the live spread for each candidate symbol with the read-only
get_symbols tool, and
convert to points (spread * point).
- Pull
atr_period + 5 completed M5 bars per symbol.
- Put the spreads in the input's
spread_points map — not in config.yaml. Spread is data;
it changes minute to minute.
- Run the evaluator once for the whole watchlist and sort the setups by
expectancy.cost_r,
ascending.
- Report
cost_r, edge_required, and expected_value_r for each. Every expected_value_r
will be negative. That is the finding, not a defect.
When expectancy.breakeven_achievable is false, the spread is so large relative to the stop
that breakeven would demand a win rate above 100%. No filter can rescue that symbol at that
stop size. Say so plainly rather than quoting edge_required as if it were a target.
volatility.atr_percent_of_price is comparable across instruments; atr_points is not. Use it
when asked for "the most volatile" symbol. Note that higher volatility raises the target and
the stop together, so it does not improve expectancy on its own — it only lowers cost_r by
making the spread a smaller share of the stop, and makes the outcome arrive sooner.
Using it as a control
When a user proposes a filter, an indicator, or a session rule that they believe adds edge,
the honest test is whether it beats this coin out of sample and net of costs. The signal's
edge_required field states the bar in percentage points of win rate. A filter tuned until it
looks good on the same data it was chosen on has not cleared it.
Boundaries
- Advisory only. Never place orders, calculate lots, or bypass account risk controls.
- Never present this strategy as tradeable, profitable, or as having any edge. It has none.
- Never omit the
expectancy block when reporting a signal.
- Do not add a daily profit target, a martingale, or unlimited re-entry on this skill's output.
Losing strategies are made worse, not better, by more trades.
- Contextual disqualifiers such as news, holidays, spread, exposure, and drawdown remain
separate checks that this evaluator does not perform.
