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Donchian Strong Regime

Organization
Superior-Trade
donchian-strong-regime

Use when writing a trend-breakdown short gated by a triple-confirmed strong-bear regime — donchian short, structural breakdown, regime-gated trend follower, EMA-separation plus ADX plus N-bar return confirmation. Validated on BTC; fires only in confirmed bear regimes and takes zero trades in chop by design. Pairs with bollinger-reverter-4h.

Overview

PublisherSuperior-Trade
Repositorysuperior-skills
Skill namedonchian-strong-regime
Stars
215
Forks
9
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Superior-Trade on GitHub. Read the source before you install it.

Installation

Install the Donchian Strong Regime AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Superior-Trade/superior-skills/tree/main/skills/donchian-strong-regime
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Superior-Trade/superior-skills.git /tmp/superior-skills
mkdir -p .claude/skills
cp -r /tmp/superior-skills/skills/donchian-strong-regime .claude/skills/donchian-strong-regime
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Donchian Strong Regime in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Donchian Strong Regime on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Donchian Strong Regime is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Donchian Strong-Regime Short

Trend-breakdown short, gated by a triple-confirmed strong-bear regime. Stays out of chop entirely. Validated on BTC/USDC:USDC over 162 days (2025-11-20 → 2026-05-01).

Searchable under: trend follower, breakdown, regime-gated, donchian short, structural break.

Backtest evidence

WindowTradesWin rateProfitMax DD
Full period (162d)6100%+6.69%0%
First-half / strong bear (82d)6100%+6.69%0%
Second-half / chop (80d)0—0%0%

The triple-confirmation gate produced zero trades in the rangy second half — exactly the behavior a regime gate should produce. Every fired trade in the first half captured the trailing stop for profit.

Thesis

In a confirmed strong-bear regime (ema separation, ADX, recent momentum all aligned), a close below the 24-bar low (4 days of structure) reliably continues lower. The gate prevents the strategy from firing during sideways/rangy markets where the same signal mean-reverts.

Mechanics

  • Pair: validated on BTC/USDC:USDC; expected to behave similarly on other deeply-liquid majors during their own confirmed bear regimes
  • Timeframe: 4h (entry signal); 4h trend indicators (regime gate)
  • Regime gate (ALL three required):
    • EMA50 / EMA200 - 1 < -0.06 (≥6% separation = deep structural downtrend, not a fresh cross)
    • ADX(14) > 25 (trend strength confirmed)
    • close.pct_change(30) < -0.10 (last 30 bars = ~5 days, actual downside momentum)
  • Entry (short): close < lowest_24_bar_low AND regime gate satisfied
  • Exit (any of):
    • close > highest_6_bar_high (24h ceiling break — local reversal)
    • 2 consecutive bars with RSI > 55 (sustained rebound)
    • Trailing stop fires (Phase 2 — see the dsl-exit-engine skill)
  • Stops: Phase 1 hard stop at -5%; Phase 2 trailing activates at +3%, trails 2% behind peak

Full strategy code

python
from freqtrade.strategy import IStrategy
import pandas as pd
import talib.abstract as ta


class DonchianStrongRegimeStrategy(IStrategy):
    INTERFACE_VERSION = 3
    timeframe = "4h"
    can_short = True

    stoploss = -0.05
    trailing_stop = True
    trailing_stop_positive = 0.02
    trailing_stop_positive_offset = 0.03
    trailing_only_offset_is_reached = True

    minimal_roi = {"0": 100.0}  # disable ROI; trailing + signal exits only
    process_only_new_candles = True
    startup_candle_count = 220
    use_exit_signal = True

    def populate_indicators(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
        dataframe["lowest_24"] = dataframe["low"].rolling(24).min().shift(1)
        dataframe["highest_6"] = dataframe["high"].rolling(6).max().shift(1)
        dataframe["ema50"] = ta.EMA(dataframe, timeperiod=50)
        dataframe["ema200"] = ta.EMA(dataframe, timeperiod=200)
        dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
        dataframe["adx"] = ta.ADX(dataframe, timeperiod=14)

        dataframe["ema_sep"] = (
            (dataframe["ema50"] - dataframe["ema200"]) / dataframe["ema200"]
        )
        dataframe["ret_30"] = dataframe["close"].pct_change(30)
        dataframe["regime_strong"] = (
            (dataframe["ema_sep"] < -0.06)
            & (dataframe["adx"] > 25)
            & (dataframe["ret_30"] < -0.10)
        )
        return dataframe

    def populate_entry_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
        cond = (
            (dataframe["close"] < dataframe["lowest_24"])
            & dataframe["regime_strong"]
        )
        dataframe.loc[cond, "enter_short"] = 1
        dataframe.loc[cond, "enter_tag"] = "donchian_strong_bear"
        return dataframe

    def populate_exit_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
        cond = (
            (dataframe["close"] > dataframe["highest_6"])
            | ((dataframe["rsi"] > 55) & (dataframe["rsi"].shift(1) > 55))
        )
        dataframe.loc[cond, "exit_short"] = 1
        return dataframe

Reference config

json
{
  "exchange": {"name": "hyperliquid", "pair_whitelist": ["BTC/USDC:USDC"]},
  "stake_currency": "USDC",
  "stake_amount": 100,
  "dry_run_wallet": {"USDC": 150},
  "timeframe": "4h",
  "max_open_trades": 1,
  "minimal_roi": {"0": 100.0},
  "stoploss": -0.05,
  "trading_mode": "futures",
  "margin_mode": "isolated",
  "entry_pricing": {"price_side": "same", "price_last_balance": 0.0},
  "exit_pricing": {"price_side": "same", "price_last_balance": 0.0},
  "pairlists": [{"method": "StaticPairList"}]
}

Honest framing

This strategy only fires during confirmed strong-bear regimes. In bull markets, sideways markets, and weak bears it will trade rarely or not at all — by design. Do not "improve" by loosening the gate; the loose-gate version (without triple confirmation) lost money in the same window.

The 100% backtest win rate is partly a function of sample size (6 trades). The honest expectation is ~60-75% win rate with similar expectancy when the gate is properly confirmed across longer windows.

Pair this strategy with bollinger-reverter-4h (the chop-regime sibling) for full-spectrum coverage — they fire on mutually exclusive regimes.

Tunables

ParameterRangeEffect
Regime EMA separation-0.04 to -0.08Looser = more trades, more chop noise; tighter = fewer, cleaner
Regime ADX threshold20 - 30Higher = more selective trend confirmation
Regime return lookback20 - 40 barsWindow for "actual momentum" check
Donchian lookback (low)18 - 36Length of structural floor
Exit lookback (high)4 - 8Tighter exit = faster wins, more giveback
Trail activate0.02 - 0.04Where Phase 2 kicks in
Trail offset0.015 - 0.025Tightness once activated

Known failure modes

  • Fresh bear regimes that haven't yet triggered the 30-bar return < -10% threshold: strategy waits until momentum is established, missing the first leg
  • Whipsaw within a strong bear: a sharp counter-rally past the 6-bar high exits the trade right before the resumption. This is the price of having tight exits
  • Alts with low liquidity: Donchian lows can be set by a single liquidation wick; restrict to majors

Pairing

  • Designed to coexist with the bollinger-reverter-4h skill (the chop-regime sibling)
  • Uses the triple-gate pattern from the regime-overlay skill
  • Uses the Phase 2 trailing stop from the dsl-exit-engine skill

Deployment recommendation

Run as its own sub-account so the regime gate's "trade nothing for weeks" behavior doesn't fight a mean-reversion strategy in the same wallet. See your Superior Trade account setup for sub-accounts.

Frequently asked questions

What does the Donchian Strong Regime AI skill do?

Use when writing a trend-breakdown short gated by a triple-confirmed strong-bear regime — donchian short, structural breakdown, regime-gated trend follower, EMA-separation plus ADX plus N-bar return confirmation. Validated on BTC; fires only in confirmed bear regimes and takes zero trades in chop by design. Pairs with bollinger-reverter-4h.

Why use Donchian Strong Regime on TypingMind?

Because you install it once and use it with any model. Donchian Strong Regime is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Donchian Strong Regime in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Superior-Trade/superior-skills/tree/main/skills/donchian-strong-regime. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Donchian Strong Regime?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Donchian Strong Regime?

As many as you like. As long as a model supports skills, you can use Donchian Strong Regime with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Donchian Strong Regime AI skill free?

Yes. It is published on GitHub by Superior-Trade under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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