Diamond Freqtrade Strategy: Backtest Results (−16.3%)

How Diamond trades

Diamond was produced by hyperparameter optimisation rather than designed by hand. Its tuned entry compares the candle high from seven bars ago with the current volume multiplied by 0.942, and its exit compares an older high with the current low. A stepped take-profit and a trailing stop do the rest.

Comparing a price to a volume figure has no market logic behind it; the rule exists because it fitted past data. Treat Diamond as an example of what over-fitted strategies look like, not as a trading idea.

Rules at a glance

Indicators: Shifted price vs. volume comparison
Take-profit (minimal ROI): 24.2% from entry; 4.4% after 13m; 2% after 51m; 0% after 2h 50m
Stoploss: −27.1%
Trailing stop: Trails 1.1% behind the high once profit passes 5.4%
Exit signal: Used

Results by month

Profit or loss in USDT for trades closed in each month.

JFMAMJJASOND2023-47+15+0+0+0+9+152024+11+31+28-35+13+25-57-52+4-0+67+162025-50-144-21-27+36

Results by coin

Pair	Trades	Win rate	Profit (USDT)	Avg. hold
LINK/USDT	3	33.3%	+2.15	8h 47m
AVAX/USDT	7	57.1%	+0.44	3d 23h
BTC/USDT	0	0.0%	0.00	0:00
XRP/USDT	0	0.0%	0.00	0:00
ADA/USDT	0	0.0%	0.00	0:00
DOGE/USDT	0	0.0%	0.00	0:00
DOT/USDT	0	0.0%	0.00	0:00
SOL/USDT	1	0.0%	−0.03	6h 10m
BNB/USDT	311	69.5%	−22.50	1d 21h
ETH/USDT	392	66.1%	−142.92	1d 18h

Why trades closed

Exit reason	Trades	Win rate	Profit (USDT)
Take-profit (ROI)	700	68.6%	+500.11
Still open at end of test	2	0.0%	−14.56
Stoploss	2	0.0%	−109.07
Trailing stop	10	0.0%	−539.36

The code

Diamond comes from the community freqtrade-strategies repository (GPL-3.0). These are the parts that decide when it trades; the full file is 155 lines.

Entry rules

def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    conditions = []
    conditions.append(
        qtpylib.crossed_above
        (
            dataframe[self.buy_fast_key.value].shift(self.buy_horizontal_push.value),
            dataframe[self.buy_slow_key.value] * self.buy_vertical_push.value
        )
    )

    if conditions:
        dataframe.loc[
            reduce(lambda x, y: x & y, conditions),
            'enter_long']=1

    return dataframe

Exit rules

def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    conditions = []
    conditions.append(
        qtpylib.crossed_below
        (
            dataframe[self.sell_fast_key.value].shift(self.sell_horizontal_push.value),
            dataframe[self.sell_slow_key.value] * self.sell_vertical_push.value
        )
    )
    if conditions:
        dataframe.loc[
            reduce(lambda x, y: x & y, conditions),
            'exit_long']=1
    return dataframe

Indicators

def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    # you can add new indicators and enable them inside
    # hyperoptable categorical params on the top
    # dataframe['ma_fast'] = ta.SMA(dataframe, timeperiod=9)
    # dataframe['ma_slow'] = ta.SMA(dataframe, timeperiod=18)
    # dataframe['{...}'] = ta.{...}(dataframe, timeperiod={...})
    return dataframe

Reproduce this test

With freqtrade installed (see our Docker guide), download the data and run the same backtest:

freqtrade download-data --exchange binance --pairs ADA/USDT AVAX/USDT BNB/USDT BTC/USDT DOGE/USDT DOT/USDT ETH/USDT LINK/USDT SOL/USDT XRP/USDT --timeframes 5m --timerange 20230501-20250530
freqtrade backtesting --strategy Diamond --timeframe 5m \
  --timerange 20230601-20250530 --dry-run-wallet 1000 --stake-amount 200 --max-open-trades 5 --fee 0.001

Results can differ slightly with a different data source or freqtrade version. See how we test.