Swing trading strategy in Python

What is swing trading?

Swing trading sits between day trading and long-term investing: positions are held from a few days to a few weeks to capture a single 'swing' in price. It needs less screen time than intraday trading and trades far more than buy-and-hold.

A robust swing approach combines a trend filter with a momentum gate: only trade in the direction of the larger trend, and avoid buying into exhausted moves. That keeps you in confirmed up-swings and out of choppy, range-bound conditions.

The logic

We define the trend with a 50-bar EMA on daily bars and only consider longs while price trades above it. This filters out the directionless markets that punish swing entries.

The rule is stateless and long-only: hold a full position while price is above the 50-EMA and the 14-period RSI sits below 60, and step aside in downtrends or once momentum runs hot. A stop-loss caps the downside on a failed swing.

swing trading in Python

Here is the full strategy in the Manifold-BT expression DSL. It imports manifoldbt, builds the signal, configures realistic execution, and runs the backtest against Rust.

import manifoldbt as mbt
from manifoldbt.indicators import close, ema, rsi
from manifoldbt.helpers import time_range, Slippage, Interval

# Daily trend filter (50-EMA) + momentum gate (14-RSI)
trend = ema(close, 50)
r = rsi(close, 14)
uptrend = close > trend

# Long while the trend is up and momentum is not yet overheated
strategy = (
    mbt.Strategy.create("swing")
    .signal("trend", trend)
    .signal("rsi", r)
    .size(mbt.when(uptrend & (r < 60.0), 1.0, 0.0))
    .stop_loss(pct=6.0)
)

start, end = time_range("2020-01-01", "2026-01-01")
config = mbt.BacktestConfig(
    universe={"binance": ["BTCUSDT"]},
    time_range_start=start,
    time_range_end=end,
    bar_interval=Interval.days(1),
    initial_capital=10_000,
    fees=mbt.FeeConfig.binance_perps(),
    slippage=Slippage.fixed_bps(2),
    warmup_bars=50,
)
store = mbt.ingest(provider="binance", symbol="BTCUSDT", symbol_id=1,
                   interval="1d", start="2020-01-01T00:00:00Z",
                   end="2026-01-01T00:00:00Z")
result = mbt.run(strategy, config, store)
print(result.summary())
mbt.plot.tearsheet(result, show=True)

Backtest it with Manifold-BT

On daily bars the strategy trades only a handful of times a year, so fees are negligible and the result is driven by how well the trend filter keeps you on the right side of the major moves.

Sweep the EMA length and the RSI gate together. Swing systems are few-trade, so judge robustness across a wide grid rather than chasing the single best pair, which tells you little.

Frequently asked questions