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Dca Weekly

Organization
Superior-Trade
dca-weekly

Use when writing, validating, or troubleshooting a recurring scheduled buy strategy (DCA, dollar-cost averaging, weekly buys, daily buys, monthly accumulation, accumulator) on Superior Trade — especially anything that should "buy more of the same pair" on a calendar trigger rather than a price trigger.

Overview

PublisherSuperior-Trade
Repositorysuperior-skills
Skill namedca-weekly
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 Dca Weekly 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/dca-weekly
  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/dca-weekly .claude/skills/dca-weekly
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dca Weekly 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 Dca Weekly 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 Dca Weekly 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.

Strategy: DCA · Scheduled Buys

When to use

A user asks to "buy X every week", "DCA into BTC", "scheduled buy", "accumulator", "monthly buy", or any variation that means open a position once, then keep adding to it on a calendar cadence. Not for "buy when price drops" — that's grid trading (see grid-trading).

What it does

  • Holds one open trade per pair (Freqtrade's hard rule).
  • The first calendar trigger opens the trade with a small initial stake.
  • Every subsequent trigger calls adjust_trade_position and adds the same notional to the open trade.
  • The position grows in fills inside a single Trade row. PnL is reported per-trade.
  • Exits only when the user sets one (typically never, for true DCA — stoploss = -0.99 and no populate_exit_trend).

Backtest reference

WindowBTC/USDC 1d, 2025-11-15 → 2026-05-01 (auto-narrowed to data availability, ~10 weeks)
Trades1 (still open at end, force-closed)
Entry orders inside the trade10 (1 initial + 9 weekly DCA, tagged weekly_dca)
Stake per buy~$36.87
Total invested~$365 of $10,000 wallet
Per-trade PnL+10.0%
Wallet PnL+0.37% / +$36.61
Holding66 days
Backtest ID01kqyz1ysdy9dyw7tbdrhz5gek

The (rejected_signals: 9) warning in logs is normal: populate_entry_trend keeps emitting Monday flags even while a trade is open, but adjust_trade_position does the actual buys.

The Freqtrade primitives that make this work

These four flags are the difference between v1 (1 trade ever, the rest rejected) and v2 (a real ladder of fills). All four are required:

python
position_adjustment_enable = True
max_entry_position_adjustment = 26   # cap on number of weekly adds
max_dca_multiplier = 27.0            # 1 initial + 26 adds

Plus two callbacks:

  • custom_stake_amount — divides the user-configured stake by max_dca_multiplier so the initial entry leaves room for the future weekly adds.
  • adjust_trade_position — the calendar trigger. Returns (stake, tag) to add, None to do nothing.

Reference implementation

python
from freqtrade.strategy import IStrategy
from freqtrade.persistence import Trade
from datetime import datetime
import pandas as pd


class WeeklyDcaBtcStrategy(IStrategy):
    minimal_roi = {"0": 100.0}   # never exit on profit target
    stoploss = -0.99             # never exit on stop
    trailing_stop = False
    timeframe = "1d"
    process_only_new_candles = True
    startup_candle_count = 5
    can_short = False

    # The piece naive translations miss.
    position_adjustment_enable = True
    max_entry_position_adjustment = 26   # ~6 months of weekly buys
    max_dca_multiplier = 27.0            # 1 initial + 26 weekly adds

    def populate_indicators(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
        dataframe["dow"] = pd.to_datetime(dataframe["date"]).dt.dayofweek
        return dataframe

    def populate_entry_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
        # Initial entry on the first Monday encountered.
        dataframe.loc[(dataframe["dow"] == 0) & (dataframe["volume"] > 0), "enter_long"] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
        return dataframe

    def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
                            proposed_stake: float, min_stake, max_stake: float,
                            leverage: float, entry_tag, side: str, **kwargs) -> float:
        # Reserve room for the future weekly adds.
        return proposed_stake / self.max_dca_multiplier

    def adjust_trade_position(self, trade: Trade, current_time: datetime,
                              current_rate: float, current_profit: float,
                              min_stake, max_stake: float,
                              current_entry_rate: float, current_exit_rate: float,
                              current_entry_profit: float, current_exit_profit: float,
                              **kwargs):
        if trade.has_open_orders:
            return None
        if current_time.weekday() != 0:   # Monday only
            return None
        # Skip the Monday on which the initial entry was placed (Freqtrade
        # calls adjust_trade_position on the same candle as the initial
        # entry; without this guard you double-buy on week 1).
        filled = trade.select_filled_orders(trade.entry_side)
        if filled:
            last_dt = filled[-1].order_filled_utc
            if last_dt and last_dt.date() == current_time.date():
                return None
        # Buy the same notional as the initial entry every Monday.
        first_stake = filled[0].stake_amount_filled if filled else (min_stake or 10)
        return (first_stake, "weekly_dca")

Config requirements

json
{
  "exchange": { "name": "hyperliquid", "pair_whitelist": ["BTC/USDC"] },
  "stake_currency": "USDC",
  "stake_amount": 1000,
  "dry_run_wallet": {"USDC": 10000},
  "timeframe": "1d",
  "max_open_trades": 1,
  "stoploss": -0.99,
  "minimal_roi": { "0": 100.0 },
  "entry_pricing": { "price_side": "same" },
  "exit_pricing": { "price_side": "same" },
  "pairlists": [{ "method": "StaticPairList" }]
}

stake_amount is the post-division budget the user wants per buy times max_dca_multiplier. With stake_amount: 1000 and max_dca_multiplier: 27, each Monday buy is ~$37; total budget is ~$1000.

dry_run_wallet must be ≥ stake_amount (Freqtrade keeps a 1% reserve, so the strict gate is stake_amount ≤ dry_run_wallet × 0.99). Default dry_run_wallet is 1000; bump it up if you raise stake.

Common pitfalls

  1. No position_adjustment_enable. Without it, repeat Monday flags are silently rejected and you get one trade ever. The classic v1 mistake.
  2. No same-day guard in adjust_trade_position. Without the filled[-1].order_filled_utc.date() == current_time.date() check, the strategy double-buys on the Monday the initial entry was placed.
  3. Forgetting to scale stake_amount. Without custom_stake_amount returning proposed_stake / max_dca_multiplier, the first buy uses the full configured stake and the wallet runs out before week 5.
  4. Using populate_exit_trend to "exit half". Doesn't work — Freqtrade only knows full exits via populate_exit_trend. Partial exits go through adjust_trade_position returning a negative stake.
  5. Setting stoploss ≥ -0.5. A real DCA isn't supposed to stop out on a 50% drawdown. Use -0.99 so the stop never triggers, then exit manually if needed.

Variants

  • Daily / monthly cadence: change current_time.weekday() != 0 to current_time.day != 1 (1st of month) or remove the guard entirely (every candle close).
  • Drawdown-aware DCA: add a check on current_profit < -0.10 to add EXTRA on top of the calendar — buy more when down 10%. Combine the calendar check with current_profit < threshold.
  • Spot vs futures: works on both. Use BTC/USDC for spot (trading_mode: "spot" or omit) or BTC/USDC:USDC for perp (trading_mode: "futures", margin_mode: "cross"). DCA is most idiomatic on spot.

Sources

Frequently asked questions

What does the Dca Weekly AI skill do?

Use when writing, validating, or troubleshooting a recurring scheduled buy strategy (DCA, dollar-cost averaging, weekly buys, daily buys, monthly accumulation, accumulator) on Superior Trade — especially anything that should "buy more of the same pair" on a calendar trigger rather than a price trigger.

Why use Dca Weekly on TypingMind?

Because you install it once and use it with any model. Dca Weekly 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 Dca Weekly in TypingMind?

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

Which AI models can use Dca Weekly?

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 Dca Weekly?

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

Is the Dca Weekly 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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