Parabolic SAR Trend Filter Strategy Backtest: BTC-USD 24.53% Return With 13 Closed Trades

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Parabolic SAR is a classic trend-following indicator. It is designed to trail price and flip when momentum changes direction. Used by itself, it can be noisy. Used with a trend filter, it becomes more selective.

This article walks through PsarTrendFilterStrategy, one of the strategies included in the Mega Backtrader Strategy Pack. The strategy was tested on BTC-USD daily candles from July 23, 2024 to July 23, 2026.

The result:

This is a useful clean example because the run ended with 13 closed trades and no open position left behind. The return was not created by an unfinished trade.

Strategy Idea

PsarTrendFilterStrategy combines a moving-average regime filter with Parabolic SAR flips.

The strategy uses three rules:

1. A 30-period simple moving average determines whether the market is in a long-biased or short-biased regime.
2. Parabolic SAR provides the entry trigger.
3. A trailing stop manages the exit after entry.

The important design choice is that the strategy does not take every Parabolic SAR flip. It only takes signals that agree with the broader moving-average trend.

Strategy Parameters

The strategy exposes a small parameter set:

params = (
    ('ma_period', 30),
    ('psar_af', 0.01),
    ('psar_afmax', 0.1),
    ('trail_percent', 0.03),
)

These settings control:

• The moving-average period used for the trend filter.
• The Parabolic SAR acceleration factor.
• The maximum acceleration factor.
• The trailing stop percentage after entry.

Code Walkthrough

The strategy starts with a moving average for trend direction:

self.sma = bt.indicators.SimpleMovingAverage(
    self.datas[0],
    period=self.p.ma_period,
)

It then creates the Parabolic SAR indicator:

self.psar = bt.indicators.ParabolicSAR(
    self.datas[0],
    af=self.p.psar_af,
    afmax=self.p.psar_afmax,
)

The entry signal is detected with a crossover between price and the Parabolic SAR value:

self.psar_cross = bt.indicators.CrossOver(
    self.data.close,
    self.psar,
)

For long trades, price must first be above the moving average:

if self.data.close[0] > self.sma[0]:
    if self.psar_cross[0] > 0.0:
        self.order = self.buy()

That means the strategy only buys when the market is already above the trend filter and price crosses above PSAR.

For short trades, price must first be below the moving average:

elif self.data.close[0] < self.sma[0]:
    if self.psar_cross[0] < 0.0:
        self.order = self.sell()

That keeps short entries aligned with a weaker trend regime.

After a completed long entry, the strategy places a trailing sell stop:

if order.isbuy():
    self.sell(
        exectype=bt.Order.StopTrail,
        trailpercent=self.p.trail_percent,
    )

After a completed short entry, it places a trailing buy stop:

elif order.issell():
    self.buy(
        exectype=bt.Order.StopTrail,
        trailpercent=self.p.trail_percent,
    )

That gives the strategy a complete structure: filter the market regime, enter on a PSAR flip, and let the trailing stop manage the exit.

Backtest Command

The article result was generated with:

python run_backtest.py --fast --fast-plots --fast-equity \
  --workers 1 \
  --symbol BTC-USD \
  --period 2y \
  --interval 1d \
  --benchmark BTC-USD \
  --stake-percent 99 \
  --strategies strategies \
  --out results \
  --strategy-filter PsarTrendFilterStrategy.py

Backtest setup:

Results

This strategy did not produce the largest return in the library, but it is a strong example of a compact, readable trading system that produced positive results with controlled drawdown and no unresolved open trade.

Equity Curve vs Benchmark

The equity curve shows the strategy growing from $10,000 to $12,452.55 while BTC buy-and-hold finished slightly negative over the same two-year window.

Drawdown vs Benchmark

The drawdown chart shows a maximum strategy drawdown of 9.30%.

Rolling Return vs Benchmark

The rolling return chart shows how the strategy's performance developed through the backtest window.

Daily Return Distribution

The daily return distribution gives a compact view of the strategy's day-to-day behavior.

What This Shows

This strategy is a good example of why small systems still matter inside a large research library.

The logic is easy to understand:

1. Use an SMA to define the regime.
2. Use PSAR to identify a directional flip.
3. Use a trailing stop to manage exits.
4. Compare the result against buy-and-hold.

Simple strategies are useful because they are easy to inspect, modify, combine, and benchmark. In a large package, they also serve as building blocks for more advanced research.

Get the Complete Strategy Library

PsarTrendFilterStrategy is one of 500+ Backtrader-ready strategies included in the Mega Backtrader Strategy Pack.

Get the full package here: Mega Backtrader Strategy Pack

The package includes:

• 500+ Python strategy modules in the strategies/ package.
• Fast batch backtesting with multiprocessing.
• Per-strategy metrics, equity curves, drawdown plots, and CSV outputs.
• A browsable HTML strategy catalog.
• A Markdown strategy package manual.
• Windows and shell batch runners for automated research.

If you want to test more strategy ideas faster, this package gives you the code and the research workflow.

Get the Mega Backtrader Strategy Pack

Disclaimer

This article is for research and educational use only. Backtest results are not financial advice, investment advice, or a guarantee of future performance. Always validate strategy logic, data quality, execution assumptions, costs, slippage, and risk before using any trading system.