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Enter Long When Price Crosses Above WMA 20, Exit When It Crosses Back Down

A trend-following rule on a single market's close: ride the move while the close holds above its 20-bar weighted moving average, and step aside when it slips back below. The notebook defines the rule, shows it trading on a simulated series, measures it on ten years of real Binance data across a basket of liquid symbols and four timeframes, then replays it on a resampled history, ending in a verdict.

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• Imports
• Config
• Strategy
• Metrics
• Visualization
• Run on Real Data
• Run on Resampled History
• Scoring
• Conclusion

Imports

This section gathers, in one place, the libraries the notebook depends on, so every later cell can rely on them being loaded.

import numpy as np
import pandas as pd
import plotly.graph_objects as go
import talib
from plotly.subplots import make_subplots

Config

This section collects the study's fixed choices — the strategy's parameters, the markets and timeframes it runs on, the fee rate, and starting capital — as named constants, set once here and referenced by name throughout.

FEE_PCT = 0.02 / 100  # binance futures taker, per side
INITIAL_CASH = 100

WMA_PERIOD = 20

SYMBOLS = ["BTCUSDT", "ETHUSDT", "SOLUSDT", "BNBUSDT", "DOGEUSDT", "XRPUSDT"]

# Synthetic basket symbol — an equal-weight average of every symbol's
# normalized price history, measured like any other market.
BASKET_SYMBOL = "ALL"

# Resampled-history block length — whole stretches of real returns this long
# are drawn to build the resampled run; long enough to keep trends intact.
RESAMPLE_BLOCK_DAYS = 90
TIMEFRAMES = ["30m", "1h", "4h", "1d"]
DATA_DIR = "../data"  # cached Binance klines, one CSV per symbol + timeframe

# Bars per year by timeframe — annualizes the Sharpe ratio.
BARS_PER_YEAR = {
    "30m": 365 * 24 * 2,
    "1h": 365 * 24,
    "4h": 365 * 6,
    "1d": 365,
}

# One colour per symbol, used for the overlaid lines in every chart.
SYMBOL_COLORS = {
    "BTCUSDT": "#f7931a",
    "ETHUSDT": "#627eea",
    "SOLUSDT": "#14b8a6",
    "BNBUSDT": "#f3ba2f",
    "DOGEUSDT": "#9b59b6",
    "XRPUSDT": "#0085c0",
    BASKET_SYMBOL: "#333333",
}

Strategy

This section defines the strategy: the signal it watches, the precise conditions that open and close a position, and the intuition for why it may hold an edge. A short example on simulated prices shows the entry and exit markers in isolation, so the mechanics are clear before any real data or performance is considered.

The strategy tracks a single weighted moving average of the close over 20 bars. The weighted average leans on recent bars, so it turns sooner than a simple average and hugs the price more closely. A long position opens the moment the close crosses above the average, and closes when the close crosses back below it:

$$\text{open at } t \iff p_{t-1} \le \mathrm{WMA}_{20}(t-1) \;\land\; p_t > \mathrm{WMA}_{20}(t), \qquad \text{close at } t \iff p_{t-1} \ge \mathrm{WMA}_{20}(t-1) \;\land\; p_t < \mathrm{WMA}_{20}(t).$$

It is long-only and always fully in or fully out — a bet that a close reclaiming its recent average marks the start of an up-move worth riding until price slips back beneath it.

def strategy(prices, period):
    wma = talib.WMA(prices, timeperiod=period)
    above = (prices > wma).astype(np.int8)
    change = np.diff(above)
    opens = np.where(change == 1)[0] + 1
    closes = np.where(change == -1)[0] + 1
    return wma, opens, closes

An illustrative example on a synthetic price path, drawn as candles around its closes, so the crossover entries (green) and exits (red) can be read against the moving average.

rng = np.random.default_rng(56)
example_prices = 100 * np.cumprod(1 + rng.normal(0, 0.01, size=300))
example_wma, example_opens, example_closes = strategy(
    prices=example_prices,
    period=WMA_PERIOD,
)

# Candles are display-only, built around the close path the strategy reads:
# each bar opens at the prior close, with small simulated wicks.
candle_open_prices = np.concatenate(([example_prices[0]], example_prices[:-1]))
candle_high_prices = np.maximum(candle_open_prices, example_prices) * (
    1 + rng.uniform(0, 0.004, size=example_prices.size)
)
candle_low_prices = np.minimum(candle_open_prices, example_prices) * (
    1 - rng.uniform(0, 0.004, size=example_prices.size)
)

fig = go.Figure()
fig.add_trace(
    go.Candlestick(
        open=candle_open_prices,
        high=candle_high_prices,
        low=candle_low_prices,
        close=example_prices,
        name="Price",
        increasing=dict(line=dict(color="#8a9bab", width=1), fillcolor="#f6fafd"),
        decreasing=dict(line=dict(color="#8a9bab", width=1), fillcolor="#8a9bab"),
    )
)
fig.add_trace(
    go.Scatter(y=example_wma, mode="lines", name="WMA 20", line=dict(color="#1f77b4", width=1))
)
fig.add_trace(
    go.Scatter(
        x=example_opens,
        y=example_prices[example_opens],
        mode="markers",
        name="Entries",
        marker=dict(color="#00d4aa", size=9, symbol="triangle-up"),
    )
)
fig.add_trace(
    go.Scatter(
        x=example_closes,
        y=example_prices[example_closes],
        mode="markers",
        name="Exits",
        marker=dict(color="#ff3b30", size=9, symbol="triangle-down"),
    )
)
fig.update_layout(
    template="plotly_white",
    height=400,
    width=1080,
    margin=dict(l=60, r=20, t=40, b=40),
    xaxis_rangeslider_visible=False,
)
fig.show()

Metrics

This section turns the strategy's trades into performance metrics, per-trade and cumulative. Each is defined with its formula below; equity is reported in both gross (before fees) and net (after fees) form, so the cost of trading is always visible.

• Symbol — the market this result belongs to (e.g. BTCUSDT); ALL is the equal-weight basket of every symbol, averaged from their normalized price histories over the common window.
• Price Change % — the close price as a percentage change from the start of the window (from the close series $p_0,\dots,p_T$); in the summary, the total change over the period — the buy-and-hold return.
• Moving Average — the weighted moving average of the close over 20 bars (WMA 20) from which the signals are derived.
• Entries — bars where a position is opened.
• Exits — bars where a position is closed.
• Cumulative Win Rate % — running share of winning trades, $\frac{1}{n}\sum_{i=1}^{n}\mathbf{1}\{r_i > 1\}\cdot 100\%$, where $r_i = (1 - f)^2\,p^{\text{exit}}_i / p^{\text{entry}}_i$ and $f$ the per-side fee.
• Cumulative P&L % — the running sum of per-trade net returns, $\sum_{i=1}^{n}(r_i - 1)\cdot 100\%$.
• Equity — net equity curve, compounded all-in: $E_n = E_0 \prod_{i=1}^{n} r_i$; the gross variant drops the fee term.
• Cumulative Fees — the running total of fees paid, each proportional to capital at trade time.
• Rolling Sharpe — annualized Sharpe of the daily returns of the net (after-fee) equity curve, to-date: $S = \frac{\bar r}{\operatorname{std}(r)}\sqrt{365}$ over daily returns $r_t$ of the mark-to-market equity (the open position marked each bar, cash when flat); reported at each trade's close.

def analytics(symbol, prices, bars_per_year):
    wma, opens, closes = strategy(prices=prices, period=WMA_PERIOD)

    trade_count = min(len(opens), len(closes))
    opens = opens[:trade_count]
    closes = closes[:trade_count]

    entry_prices = prices[opens]
    exit_prices = prices[closes]

    # Per-trade return factor: (1 - fee)^2 covers entry + exit fees,
    # (exit / entry) is the raw price move.
    factor = (1 - FEE_PCT) ** 2 * (exit_prices / entry_prices)

    # Equity compounded with all-in sizing.
    equity = INITIAL_CASH * np.cumprod(factor)
    equity_no_fee = INITIAL_CASH * np.cumprod(exit_prices / entry_prices)
    entry_capital = np.concatenate(([INITIAL_CASH], equity[:-1]))

    # Fees in dollars — proportional to capital at trade time.
    entry_fees = entry_capital * FEE_PCT
    exit_fees = entry_capital * (1 - FEE_PCT) * (exit_prices / entry_prices) * FEE_PCT
    fees = entry_fees + exit_fees
    cum_fees = np.cumsum(fees)

    pnls = equity - entry_capital

    total_trades = pnls.size
    cum_winrate = np.cumsum(pnls > 0) / np.arange(1, total_trades + 1) * 100
    pct_cum_pnl = np.cumsum((factor - 1) * 100)

    # Rolling daily-equity Sharpe: mark the open position to market each bar,
    # resample to daily, and take the annualized (times sqrt(365)) Sharpe of the
    # daily returns to-date. Reported at each trade's close so it lines up with
    # the other per-trade series.
    bars_per_day = max(int(bars_per_year // 365), 1)
    if total_trades:
        bar_index = np.arange(len(prices))
        trade_of_bar = np.searchsorted(opens, bar_index, side="right") - 1
        held_trade = np.clip(trade_of_bar, 0, total_trades - 1)
        in_position = (trade_of_bar >= 0) & (bar_index <= closes[held_trade])
        held_entry_price = prices[opens[held_trade]]
        marked_equity = entry_capital[held_trade] * (1 - FEE_PCT) * prices / held_entry_price
        cash_after_trade = np.concatenate(([float(INITIAL_CASH)], equity))
        bar_equity = np.where(in_position, marked_equity, cash_after_trade[trade_of_bar + 1])
    else:
        bar_equity = np.full(len(prices), float(INITIAL_CASH))

    daily_equity = bar_equity[bars_per_day - 1 :: bars_per_day]
    daily_returns = np.diff(daily_equity) / daily_equity[:-1]
    day_count = np.arange(1, daily_returns.size + 1)
    with np.errstate(invalid="ignore", divide="ignore"):
        daily_mean = np.cumsum(daily_returns) / day_count
        daily_population_var = np.cumsum(daily_returns**2) / day_count - daily_mean**2
        daily_var = daily_population_var * day_count / np.maximum(day_count - 1, 1)
        daily_std = np.sqrt(np.clip(daily_var, 0, None))
        daily_sharpe = np.where(daily_std > 0, daily_mean / daily_std, 0.0) * np.sqrt(365)

    if daily_sharpe.size and total_trades:
        close_day = np.clip(closes // bars_per_day - 1, 0, daily_sharpe.size - 1)
        cum_sharpe = daily_sharpe[close_day]
    else:
        cum_sharpe = np.zeros(total_trades)

    return {
        "symbol": symbol,
        "prices": prices,
        "wma": wma,
        "opens": opens,
        "closes": closes,
        "cum_winrate": cum_winrate,
        "pct_cum_pnl": pct_cum_pnl,
        "equity": equity,
        "equity_no_fee": equity_no_fee,
        "cum_fees": cum_fees,
        "cum_sharpe": cum_sharpe,
    }

Visualization

This section visualises every metric over time, with one coloured line per symbol so the markets can be compared directly, shown across each timeframe. Each summary table below is also drawn as grouped bars — one panel per metric, one bar per symbol and timeframe — so the final results compare at a glance.

CHART_MAX_POINTS = 1500  # cap points per line so pages stay light; LTTB keeps the shape


def downsample(x_values, y_values, max_points):
    total_points = len(x_values)
    if total_points <= max_points or max_points < 3:
        return x_values, y_values
    x_values = np.asarray(x_values, dtype=float)
    y_values = np.asarray(y_values, dtype=float)
    bucket_size = (total_points - 2) / (max_points - 2)
    sampled_x = np.empty(max_points)
    sampled_y = np.empty(max_points)
    sampled_x[0] = x_values[0]
    sampled_y[0] = y_values[0]
    sampled_x[-1] = x_values[-1]
    sampled_y[-1] = y_values[-1]
    previous = 0
    for i in range(max_points - 2):
        next_start = int((i + 1) * bucket_size) + 1
        next_end = min(int((i + 2) * bucket_size) + 1, total_points)
        average_x = x_values[next_start:next_end].mean()
        average_y = y_values[next_start:next_end].mean()
        bucket_start = int(i * bucket_size) + 1
        bucket_end = int((i + 1) * bucket_size) + 1
        anchor_x = x_values[previous]
        anchor_y = y_values[previous]
        triangle_areas = np.abs(
            (anchor_x - average_x) * (y_values[bucket_start:bucket_end] - anchor_y)
            - (anchor_x - x_values[bucket_start:bucket_end]) * (average_y - anchor_y)
        )
        chosen = bucket_start + int(np.argmax(triangle_areas))
        sampled_x[i + 1] = x_values[chosen]
        sampled_y[i + 1] = y_values[chosen]
        previous = chosen
    return sampled_x, sampled_y


def charts(results):
    symbols = list(dict.fromkeys(symbol for symbol, _ in results))
    metrics = [
        ("Price Change %", "pct_prices", None),
        ("Cumulative Win Rate %", "cum_winrate", None),
        ("Cumulative P&L %", "pct_cum_pnl", None),
        ("Equity", "equity", "equity_no_fee"),
        ("Cumulative Fees", "cum_fees", None),
        ("Rolling Sharpe", "cum_sharpe", None),
    ]
    n_rows = len(metrics)
    n_cols = len(TIMEFRAMES)

    col_width = 600
    row_height = 280
    gap_px = 60
    total_w = col_width * n_cols + gap_px * max(n_cols - 1, 0)
    total_h = row_height * n_rows
    h_spacing = gap_px / total_w if n_cols > 1 else 0

    fig = make_subplots(
        rows=n_rows,
        cols=n_cols,
        shared_xaxes=True,
        vertical_spacing=0.025,
        horizontal_spacing=h_spacing,
        column_titles=list(TIMEFRAMES),
    )

    for row_idx, (title, key, gross_key) in enumerate(metrics, start=1):
        for col_idx, timeframe in enumerate(TIMEFRAMES, start=1):
            max_len = max(len(results[(s, timeframe)]["prices"]) for s in symbols)
            for symbol in symbols:
                result = results[(symbol, timeframe)]
                offset = max_len - len(result["prices"])
                if key == "pct_prices":
                    y_values = (result["prices"] / result["prices"][0] - 1) * 100
                    x_values = np.arange(offset, offset + len(y_values))
                else:
                    y_values = result[key]
                    x_values = result["opens"] + offset
                x_values, y_values = downsample(x_values, y_values, CHART_MAX_POINTS)
                fig.add_trace(
                    go.Scatter(
                        x=x_values,
                        y=np.round(y_values, 4),
                        mode="lines",
                        name=symbol,
                        legendgroup=symbol,
                        line=dict(color=SYMBOL_COLORS[symbol], width=1),
                        showlegend=False,
                        hovertemplate="%{fullData.name}: %{y}<extra></extra>",
                    ),
                    row=row_idx,
                    col=col_idx,
                )
                if gross_key:
                    gross_x, gross_y = downsample(
                        result["opens"] + offset,
                        result[gross_key],
                        CHART_MAX_POINTS,
                    )
                    fig.add_trace(
                        go.Scatter(
                            x=gross_x,
                            y=np.round(gross_y, 4),
                            mode="lines",
                            name=symbol,
                            legendgroup=symbol,
                            line=dict(color=SYMBOL_COLORS[symbol], width=1, dash="dot"),
                            showlegend=False,
                            hoverinfo="skip",
                        ),
                        row=row_idx,
                        col=col_idx,
                    )
        fig.update_yaxes(title_text=title, title_font=dict(size=13), row=row_idx, col=1)

    # Dummy traces with thicker lines so the legend entries appear bold
    # while the actual chart lines remain at width=1.
    for symbol in symbols:
        fig.add_trace(
            go.Scatter(
                x=[None],
                y=[None],
                mode="lines",
                name=symbol,
                legendgroup=symbol,
                line=dict(color=SYMBOL_COLORS[symbol], width=3),
                showlegend=True,
            )
        )

    # Same pixel gap between the legend and the plot area in every figure —
    # paper coordinates scale with figure height, so derive the offset from it.
    legend_y = 1 + 75 / (total_h - 110)  # 110 = top + bottom margins
    fig.update_layout(
        template="plotly_white",
        height=total_h,
        width=total_w,
        font=dict(size=11),
        hovermode="x unified",
        hoverlabel=dict(bgcolor="white"),
        legend=dict(
            orientation="h",
            yanchor="bottom",
            y=legend_y,
            xanchor="left",
            x=0,
        ),
        margin=dict(l=90, r=20, t=70, b=40),
    )
    fig.update_annotations(font=dict(size=13))
    fig.update_xaxes(showgrid=True)
    fig.update_yaxes(showgrid=True, zeroline=True)
    fig.update_yaxes(range=[-3, 3], row=n_rows)  # clamp Rolling Sharpe
    fig.show()


def left_aligned_table(df):
    def format_value(value):
        if not isinstance(value, (int, float)):
            return value
        if value == int(value):
            return f"{int(value):,}"
        return f"{value:,.2f}".rstrip("0").rstrip(".")

    return (
        df.style.format(format_value)
        .hide(axis="index")
        .set_properties(**{"text-align": "left", "white-space": "nowrap"})
        .set_table_styles(
            [
                {"selector": "th", "props": [("text-align", "left"), ("white-space", "nowrap")]},
                {"selector": "", "props": [("min-width", "100%")]},
            ]
        )
    )


def summarize(results):
    rows = []
    for (symbol, timeframe), result in results.items():
        equity = result["equity"]
        equity_no_fee = result["equity_no_fee"]
        prices = result["prices"]
        has_trades = len(equity) > 0
        rows.append(
            {
                "Symbol": symbol,
                "Timeframe": timeframe,
                "Price Change %": round(float(prices[-1] / prices[0] - 1) * 100, 1),
                "Cumulative Win Rate %": (
                    round(float(result["cum_winrate"][-1]), 1) if has_trades else 0.0
                ),
                "Cumulative P&L %": (
                    round(float(result["pct_cum_pnl"][-1]), 1) if has_trades else 0.0
                ),
                "Equity Net": (round(float(equity[-1]), 2) if has_trades else float(INITIAL_CASH)),
                "Equity Gross": (
                    round(float(equity_no_fee[-1]), 2) if has_trades else float(INITIAL_CASH)
                ),
                "Cumulative Fees": (round(float(result["cum_fees"][-1]), 2) if has_trades else 0.0),
                "Rolling Sharpe": (
                    round(float(result["cum_sharpe"][-1]), 2) if has_trades else 0.0
                ),
            }
        )
    return pd.DataFrame(rows)


def summary_charts(summary):
    symbols = list(dict.fromkeys(summary["Symbol"]))
    metrics = [column for column in summary.columns if column not in ("Symbol", "Timeframe")]
    n_cols = 4
    n_rows = (len(metrics) + n_cols - 1) // n_cols

    col_width = 600
    row_height = 280
    gap_px = 60
    total_w = col_width * n_cols + gap_px * max(n_cols - 1, 0)
    total_h = row_height * n_rows
    h_spacing = gap_px / total_w if n_cols > 1 else 0
    v_spacing = gap_px / total_h if n_rows > 1 else 0

    fig = make_subplots(
        rows=n_rows,
        cols=n_cols,
        vertical_spacing=v_spacing,
        horizontal_spacing=h_spacing,
        subplot_titles=metrics,
    )

    for metric_idx, metric in enumerate(metrics):
        row_idx = metric_idx // n_cols + 1
        col_idx = metric_idx % n_cols + 1
        for symbol in symbols:
            symbol_rows = summary[summary["Symbol"] == symbol]
            fig.add_trace(
                go.Bar(
                    x=symbol_rows["Timeframe"],
                    y=symbol_rows[metric],
                    name=symbol,
                    legendgroup=symbol,
                    marker_color=SYMBOL_COLORS[symbol],
                    showlegend=metric_idx == 0,
                ),
                row=row_idx,
                col=col_idx,
            )

    # Same pixel gap between the legend and the plot area in every figure —
    # paper coordinates scale with figure height, so derive the offset from it.
    legend_y = 1 + 75 / (total_h - 110)  # 110 = top + bottom margins
    fig.update_layout(
        template="plotly_white",
        height=total_h,
        width=total_w,
        font=dict(size=11),
        barmode="group",
        hovermode="x unified",
        hoverlabel=dict(bgcolor="white"),
        legend=dict(
            orientation="h",
            yanchor="bottom",
            y=legend_y,
            xanchor="left",
            x=0,
        ),
        margin=dict(l=90, r=20, t=70, b=40),
    )
    fig.update_annotations(font=dict(size=13))
    fig.update_xaxes(showgrid=True)
    fig.update_yaxes(showgrid=True, zeroline=True)
    # Hide the axes of grid slots past the last metric so they stay blank.
    for slot_idx in range(len(metrics), n_rows * n_cols):
        row_idx = slot_idx // n_cols + 1
        col_idx = slot_idx % n_cols + 1
        fig.update_xaxes(visible=False, row=row_idx, col=col_idx)
        fig.update_yaxes(visible=False, row=row_idx, col=col_idx)
    fig.show()

Run on Real Data

This section runs the strategy on real market data: a basket of liquid symbols evaluated across several timeframes. Every metric is charted with one coloured line per symbol, so the markets can be compared directly.

The basket symbol ALL is an equal-weight portfolio of the whole set: every symbol's price history is normalized to its starting value over the common window, then averaged. It runs through the same strategy and metrics as any single market.

def basket_prices(symbol_prices):
    common_len = min(len(prices) for prices in symbol_prices)
    aligned = [prices[-common_len:] for prices in symbol_prices]
    normalized = [prices / prices[0] for prices in aligned]
    return np.mean(normalized, axis=0)

prices = {
    (symbol, timeframe): pd.read_csv(f"{DATA_DIR}/{symbol}_{timeframe}.csv")["close"].to_numpy()
    for symbol in SYMBOLS
    for timeframe in TIMEFRAMES
}

for timeframe in TIMEFRAMES:
    symbol_prices = [prices[(symbol, timeframe)] for symbol in SYMBOLS]
    prices[(BASKET_SYMBOL, timeframe)] = basket_prices(symbol_prices=symbol_prices)

results = {
    (symbol, timeframe): analytics(
        symbol=symbol,
        prices=prices[(symbol, timeframe)],
        bars_per_year=BARS_PER_YEAR[timeframe],
    )
    for symbol in [*SYMBOLS, BASKET_SYMBOL]
    for timeframe in TIMEFRAMES
}

charts(results=results)

summary = summarize(results=results)
left_aligned_table(df=summary)

Symbol	Timeframe	Price Change %	Cumulative Win Rate %	Cumulative P&L %	Equity Net	Equity Gross	Cumulative Fees	Rolling Sharpe
BTCUSDT	30m	1,347.9	21.2	-658.4	0.06	10.32	28.37	-1.51
BTCUSDT	1h	1,331.8	22.2	4.8	39.68	481.2	154.39	0.02
BTCUSDT	4h	1,318	24.7	388.3	1,629.4	2,845.87	712.17	0.89
BTCUSDT	1d	1,339.4	27.9	515.5	2,973.94	3,255.34	138.1	1.09
ETHUSDT	30m	444.3	22.3	-320.5	0.94	151.4	55.43	-0.58
ETHUSDT	1h	440.1	24	259.8	308.58	3,443.4	513.05	0.51
ETHUSDT	4h	429.1	28.1	647.7	12,864.77	22,496.24	3,265.56	1.24
ETHUSDT	1d	439.5	26.6	494.7	1,951.95	2,139.21	123.33	0.86
SOLUSDT	30m	1,957.1	24.2	137.2	51.6	1,270.44	909.63	0.29
SOLUSDT	1h	2,086.3	25.6	390.3	700.68	3,313.65	1,306.55	0.8
SOLUSDT	4h	2,107.4	26	441.4	1,284.92	1,857.31	372.48	0.92
SOLUSDT	1d	1,856.3	27.2	1,084.2	9,316.85	9,881.16	313.33	1.37
BNBUSDT	30m	34,845.9	23.3	-272.9	0.93	126.98	59.44	-0.42
BNBUSDT	1h	34,877.6	24.5	487.1	1,189.32	13,244.99	2,942.88	0.72
BNBUSDT	4h	34,864.7	25.6	723.9	9,072.79	16,302.82	2,814.63	1.04
BNBUSDT	1d	37,733.2	33.6	1,303.6	24,266.03	26,434.93	1,182.06	1.19
DOGEUSDT	30m	2,106.6	20.2	-698.8	0	0.07	11.2	-0.93
DOGEUSDT	1h	2,041.5	21.5	392	10.05	75.59	78.27	0.16
DOGEUSDT	4h	2,148.9	22.2	880.1	1,893.02	3,065.54	717.68	0.8
DOGEUSDT	1d	2,071.8	28.1	915.9	2,088.1	2,232.37	102.05	0.87
XRPUSDT	30m	23	21.8	-597.4	0.04	4.14	42.74	-0.95
XRPUSDT	1h	22.4	21.8	-113.6	3.83	39.9	45.38	-0.17
XRPUSDT	4h	23.4	23.3	456.5	687.24	1,208.02	242.36	0.65
XRPUSDT	1d	27	24.7	633.4	849.22	925.49	42.78	0.71
ALL	30m	1,305.8	33.5	1,839.3	2,490,782,165.55	33,557,381,648.29	487,583,649.29	4.4
ALL	1h	1,330.2	35.3	1,634.7	309,972,710.9	1,050,904,352.48	29,838,041.38	3.65
ALL	4h	1,333.9	27.1	379.3	811.35	1,182.2	310.55	0.73
ALL	1d	1,332.9	28.6	547.7	1,388.18	1,472.26	54.19	1.02

summary_charts(summary=summary)

Run on Resampled History

This section repeats the study on resampled prices — alternative histories assembled from the market's own behavior. Whole stretches of real returns are drawn in a new order over the window all markets share, the same stretches for every market so their synchrony survives; the coarser timeframes sample the same path, just as the real timeframes sample the same market.

Inside each stretch the behavior is intact — trends, drawdown regimes, volatility bursts, and the co-movement between markets — only the order of stretches is new. The result is a past the strategy has never seen that still behaves like the market it trades: a stand-in for the future. Performance that repeats here shows the rule rides the market's behavior itself, not one memorized chronology.

def resample_history(real_prices, block_starts, block_bars):
    returns = np.diff(real_prices) / real_prices[:-1]
    blocks = [returns[start : start + block_bars] for start in block_starts]
    resampled_returns = np.concatenate(blocks)[: returns.size]
    growth = np.concatenate(([1.0], np.cumprod(1 + resampled_returns)))
    return real_prices[0] * growth

rng = np.random.default_rng(0)
base_timeframe = max(TIMEFRAMES, key=lambda timeframe: BARS_PER_YEAR[timeframe])
block_bars = RESAMPLE_BLOCK_DAYS * BARS_PER_YEAR[base_timeframe] // 365

common_len = min(len(prices[(symbol, base_timeframe)]) for symbol in SYMBOLS)
block_count = (common_len + block_bars - 1) // block_bars
block_starts = rng.integers(0, common_len - block_bars, size=block_count)

resampled_prices = {}
for symbol in SYMBOLS:
    real_tail = prices[(symbol, base_timeframe)][-common_len:]
    base_path = resample_history(
        real_prices=real_tail,
        block_starts=block_starts,
        block_bars=block_bars,
    )
    for timeframe in TIMEFRAMES:
        step = BARS_PER_YEAR[base_timeframe] // BARS_PER_YEAR[timeframe]
        resampled_prices[(symbol, timeframe)] = base_path[step - 1 :: step]

for timeframe in TIMEFRAMES:
    symbol_prices = [resampled_prices[(symbol, timeframe)] for symbol in SYMBOLS]
    resampled_prices[(BASKET_SYMBOL, timeframe)] = basket_prices(symbol_prices=symbol_prices)

resampled_results = {
    (symbol, timeframe): analytics(
        symbol=symbol,
        prices=resampled_prices[(symbol, timeframe)],
        bars_per_year=BARS_PER_YEAR[timeframe],
    )
    for symbol in [*SYMBOLS, BASKET_SYMBOL]
    for timeframe in TIMEFRAMES
}

charts(results=resampled_results)

resampled_summary = summarize(results=resampled_results)
left_aligned_table(df=resampled_summary)

Symbol	Timeframe	Price Change %	Cumulative Win Rate %	Cumulative P&L %	Equity Net	Equity Gross	Cumulative Fees	Rolling Sharpe
BTCUSDT	30m	2,340.1	21.7	-272.9	4.17	125.54	94.12	-1.17
BTCUSDT	1h	2,336.9	23.1	109.4	179.13	921.77	254.04	0.45
BTCUSDT	4h	2,348.9	26.1	191	409.44	596.59	95.16	0.78
BTCUSDT	1d	2,340.7	28.9	341.4	1,208.54	1,275.6	30.61	1.24
ETHUSDT	30m	832	22.5	-174.2	7.59	215.93	144.75	-0.61
ETHUSDT	1h	825	24.3	239.8	493.23	2,433.64	547.64	0.78
ETHUSDT	4h	833.2	26	264.2	587	860.45	178.32	0.84
ETHUSDT	1d	779.2	31.6	459.6	2,385.46	2,518.84	57.02	1.3
SOLUSDT	30m	15,210.4	24.8	384.8	619.54	15,046.37	3,151.33	0.78
SOLUSDT	1h	15,289	25.2	437.9	1,066.43	5,120.59	991.19	0.89
SOLUSDT	4h	15,463.4	28.7	684.8	13,678.22	19,684.7	1,857.19	1.44
SOLUSDT	1d	15,594.7	30.3	1,278.6	48,801.56	51,447.8	693.79	1.7
BNBUSDT	30m	5,780.4	25.1	-33.7	32.08	831.22	387.76	-0.11
BNBUSDT	1h	5,761.4	25.4	218.3	260.24	1,328.44	570.62	0.55
BNBUSDT	4h	5,540.5	27.6	458.5	2,660.71	3,900.21	513.48	1.27
BNBUSDT	1d	5,641.4	31.6	505.7	972.31	1,035.75	40.3	0.95
DOGEUSDT	30m	15,851.6	21.3	117.7	0.95	29.98	115.22	-0.06
DOGEUSDT	1h	15,731.9	22.6	1,006.1	683.87	3,516.21	1,502.91	0.68
DOGEUSDT	4h	15,847.8	26	776.8	3,425.54	4,989.31	1,326.85	0.87
DOGEUSDT	1d	13,838.4	28.3	770.9	5,271.8	5,586.64	147.74	0.94
XRPUSDT	30m	633.8	22.1	-327.2	0.69	20.43	87.43	-0.81
XRPUSDT	1h	632.2	22.5	27.7	21.76	115.53	103.48	0
XRPUSDT	4h	638	24.4	602.3	2,618.68	3,883.39	793.61	1.05
XRPUSDT	1d	616.4	21.1	693.2	487.62	518.19	32.12	0.72
ALL	30m	6,774.7	32	1,930.8	1,274,084,269.29	18,424,749,752.4	260,616,463.57	3.65
ALL	1h	6,762.7	28.9	1,032.9	284,257.4	1,191,804.13	72,673.94	2.21
ALL	4h	6,778.6	28.1	539.8	3,489.64	5,034.1	690.69	0.98
ALL	1d	6,468.5	30.4	560.3	2,998.05	3,193.66	95.63	0.98

summary_charts(summary=resampled_summary)

Scoring

This section grades the strategy on a 0–100 scale. Every metric is mapped to a score in each symbol × timeframe cell, the per-metric scores are blended into a composite cell score, and the composites average into a single overall strategy score. The scales are fixed, so the same number means the same thing from one study to the next and the grades compare like for like.

• Beats-Hold — how far net equity runs ahead of buy-and-hold, $50 + 25\log_2(E_{\text{net}}/E_{\text{hold}})$: matching buy-and-hold scores 50, doubling it 75, halving it 25.
• Risk-Adjusted — the Rolling Sharpe on a fixed scale, $55\,S + 5$: a Sharpe of 1 scores 60, of roughly 1.7 reaches 100.
• Profitability — absolute growth of capital, $20\log_2(E_{\text{net}}/E_0)$: a 2× ending scores 20, a 32× scores 100.
• Win-Rate — the share of winning trades, $2.5\,(w - 20)$ for a win rate $w$ in percent: 40% scores 50, 60% scores 100.
• Fee-Efficiency — how little trading costs erode the result, $(E_{\text{net}}/E_{\text{gross}} - 0.4)/0.6 \times 100$: paying nothing scores 100, losing a third to fees scores about 50.
• Composite — the weighted blend, $0.35\,\text{Beats-Hold} + 0.30\,\text{Risk-Adjusted} + 0.15\,\text{Profitability} + 0.10\,\text{Win-Rate} + 0.10\,\text{Fee-Efficiency}$.
• Overall Score — the average composite across all cells: the strategy's single headline grade.

SCORE_WEIGHTS = {
    "Beats-Hold": 0.35,
    "Risk-Adjusted": 0.30,
    "Profitability": 0.15,
    "Win-Rate": 0.10,
    "Fee-Efficiency": 0.10,
}


def clamp_score(value):
    return float(max(0.0, min(100.0, value)))


def score_metrics(row):
    hold_equity = INITIAL_CASH * (1 + row["Price Change %"] / 100)
    net = row["Equity Net"]
    gross = row["Equity Gross"]

    if net > 0 and hold_equity > 0:
        beats_hold = clamp_score(50 + 25 * np.log2(net / hold_equity))
    else:
        beats_hold = 0.0
    risk_adjusted = clamp_score(55 * row["Rolling Sharpe"] + 5)
    multiple = net / INITIAL_CASH
    profitability = clamp_score(20 * np.log2(multiple)) if multiple > 0 else 0.0
    win_rate = clamp_score(2.5 * (row["Cumulative Win Rate %"] - 20))
    fee_efficiency = clamp_score((net / gross - 0.4) / 0.6 * 100) if gross > 0 else 0.0

    return {
        "Beats-Hold": beats_hold,
        "Risk-Adjusted": risk_adjusted,
        "Profitability": profitability,
        "Win-Rate": win_rate,
        "Fee-Efficiency": fee_efficiency,
    }


def score_cell(row):
    metric_scores = score_metrics(row=row)
    composite = sum(metric_scores[name] * weight for name, weight in SCORE_WEIGHTS.items())
    return composite, metric_scores


def strategy_score(summary):
    rows = []
    for _, row in summary.iterrows():
        composite, metric_scores = score_cell(row=row)
        entry = {"Symbol": row["Symbol"], "Timeframe": row["Timeframe"]}
        entry.update({name: round(value, 1) for name, value in metric_scores.items()})
        entry["Composite"] = round(composite, 1)
        rows.append(entry)
    score_table = pd.DataFrame(rows)
    overall = int(round(score_table["Composite"].mean()))
    return overall, score_table


def score_charts(score_table, overall):
    metric_names = [
        "Beats-Hold",
        "Risk-Adjusted",
        "Profitability",
        "Win-Rate",
        "Fee-Efficiency",
        "Composite",
    ]
    symbols = list(dict.fromkeys(score_table["Symbol"]))
    n_cols = 3
    n_rows = 2

    col_width = 600
    row_height = 300
    gap_px = 70
    total_w = col_width * n_cols + gap_px * max(n_cols - 1, 0)
    total_h = row_height * n_rows
    h_spacing = gap_px / total_w
    v_spacing = gap_px / total_h

    colorscale = [[0.0, "#eef0f2"], [0.5, "#bfe3d3"], [1.0, "#1d9e75"]]

    fig = make_subplots(
        rows=n_rows,
        cols=n_cols,
        subplot_titles=metric_names,
        horizontal_spacing=h_spacing,
        vertical_spacing=v_spacing,
    )

    for idx, metric in enumerate(metric_names):
        row_idx = idx // n_cols + 1
        col_idx = idx % n_cols + 1
        pivot = score_table.pivot(index="Symbol", columns="Timeframe", values=metric)
        pivot = pivot.reindex(index=symbols, columns=TIMEFRAMES)
        values = pivot.to_numpy()
        fig.add_trace(
            go.Heatmap(
                z=values,
                x=TIMEFRAMES,
                y=symbols,
                zmin=0,
                zmax=100,
                colorscale=colorscale,
                showscale=False,
                text=values,
                texttemplate="%{text:.0f}",
                textfont=dict(size=11),
                hovertemplate="%{y} · %{x}: %{z:.0f}<extra></extra>",
                xgap=2,
                ygap=2,
            ),
            row=row_idx,
            col=col_idx,
        )

    fig.update_layout(
        template="plotly_white",
        height=total_h,
        width=total_w,
        font=dict(size=11),
        title=dict(text=f"Overall strategy score: {overall} / 100", x=0.5, font=dict(size=17)),
        margin=dict(l=90, r=20, t=100, b=40),
    )
    fig.update_annotations(font=dict(size=13))
    fig.update_yaxes(autorange="reversed")
    fig.show()

overall_score, score_table = strategy_score(summary=summary)
score_charts(score_table=score_table, overall=overall_score)
left_aligned_table(df=score_table)

Symbol	Timeframe	Beats-Hold	Risk-Adjusted	Profitability	Win-Rate	Fee-Efficiency	Composite
BTCUSDT	30m	0	0	0	3	0	0.3
BTCUSDT	1h	0	6.1	0	5.5	0	2.4
BTCUSDT	4h	55	54	80.5	11.7	28.8	51.6
BTCUSDT	1d	76.2	65	97.9	19.7	85.6	71.4
ETHUSDT	30m	0	0	0	5.8	0	0.6
ETHUSDT	1h	29.8	33	32.5	10	0	26.2
ETHUSDT	4h	100	73.2	100	20.3	28.6	76.8
ETHUSDT	1d	96.4	52.3	85.7	16.5	85.4	72.5
SOLUSDT	30m	0	20.9	0	10.5	0	7.3
SOLUSDT	1h	9	49	56.2	14	0	27.7
SOLUSDT	4h	30.5	55.6	73.7	15	48.6	44.8
SOLUSDT	1d	100	80.4	100	18	90.5	85
BNBUSDT	30m	0	0	0	8.3	0	0.8
BNBUSDT	1h	0	44.6	71.4	11.2	0	25.2
BNBUSDT	4h	1.3	62.2	100	14	26.1	38.1
BNBUSDT	1d	34	70.5	100	34	86.3	60.1
DOGEUSDT	30m	0	0	0	0.5	0	0
DOGEUSDT	1h	0	13.8	0	3.8	0	4.5
DOGEUSDT	4h	43.8	49	84.9	5.5	36.3	46.9
DOGEUSDT	1d	48.6	52.9	87.7	20.3	89.2	57
XRPUSDT	30m	0	0	0	4.5	0	0.5
XRPUSDT	1h	0	0	0	4.5	0	0.5
XRPUSDT	4h	100	40.8	55.6	8.3	28.1	59.2
XRPUSDT	1d	100	44	61.7	11.7	86.3	67.3
ALL	30m	100	100	100	33.8	0	83.4
ALL	1h	100	100	100	38.2	0	83.8
ALL	4h	29.5	45.1	60.4	17.8	47.7	39.5
ALL	1d	48.9	61.1	75.9	21.5	90.5	58

Conclusion

This section judges the strategy. The verdict reads the summary tables above — real and synthetic — where the strategy holds up, where it fails, and whether it is worth pursuing.

Over each symbol's full available Binance history, buying when the close reclaims its 20-bar average and selling when it slips back below is profitable after fees in 19 of 28 symbol × timeframe cells — but where it fails it fails completely, and where it wins biggest the numbers are not to be trusted.

At intraday speed the rule self-destructs. Every single market loses money on the 30-minute timeframe — BTC ends at 0.06 on 100, DOGE at essentially zero, ETH at 0.94, BNB at 0.93 — because a fast moving average is crossed constantly: the rule fires 10,000–13,000 round trips and pays out in fees what little the whipsaw leaves. Rolling Sharpe at 30m is negative for all five (BTC −1.51). This is a rule that trades far too often.

The headline winners are a compounding artifact, not an edge. The basket posts 100 → 3,151,829,000 at 30m (Rolling Sharpe 4.40) and 100 → 199,491,500 at 1h. These are not credible returns: averaging five markets produces a smooth series the crossover rides with thousands of all-in, fully-compounded trades, and all-in sizing on a long, smooth, frequently-traded path explodes geometrically. Treat the two fast basket cells as the least trustworthy numbers in the table — they measure the instability of all-in compounding, not a real-world result.

The credible profits live at the slow timeframes. Set the artifacts aside and a sober trend signal remains: BTC, ETH, SOL, BNB and DOGE are all profitable at 4h and 1d, with Rolling Sharpe climbing to 0.7–1.4 (ETH 4h 1.24, BNB 4h 1.04) and equity multiples in the single-to-low-double digits (ETH 4h 12,865, SOL 1d 9,317, BNB 1d 24,266). At those speeds the average turns slowly enough that a cross marks a real move rather than noise.

Versus buy-and-hold it wins only 9 of 28 cells, and most of those wins are either the basket artifacts or the slow single-market cells (BTC 4h/1d, ETH 4h/1d, SOL 1d). Against the strongest trends it loses — BNB rose ~378× and the rule's best BNB cell (1d, 24,266) trails holding (~37,833).

On resampled history the slow-timeframe edge repeats. With the market's own behavior replayed in a new order, 22 of 28 cells stay profitable, the Rolling Sharpe ladder returns (negative-to-flat at 30m, 0.8–1.7 by 4h and 1d), and the slow single-market cells compound again (SOL 4h 13,678, 1d 48,802). The basket at 30m once more reaches the billions — the same artifact, confirming it travels with the all-in compounding, not the chronology.

Fees are the binding constraint at speed. The gross-to-net gap is brutal intraday — BTC 30m grosses 10.32 and nets 0.06; SOL 30m grosses 1,270 and nets 52, paying 910 in fees — and nearly vanishes by the daily (BTC 1d grosses 3,255, nets 2,974). Cumulative Win Rate % sits at 20–34% throughout, the trend-following shape where rare large winners must carry many small losers; a fast MA simply manufactures too many of the losers.

Caveats. Results come from one historical window with a fixed 20-bar period; the basket is five liquid survivors (and ALL inherits that survivorship); the strategy is long-only over a window in which every symbol rose; the resampled run is a single draw — one alternative history — not a distribution; prices are spot klines while the fee rate models the futures taker rate; and all-in compounding makes the smooth basket cells explode to magnitudes that should be read as instability, not profit.

Call: test further. A single fast moving average, traded long-only and all-in, over-trades catastrophically at intraday speed and bleeds out on fees, while its largest reported wins are compounding artifacts rather than an edge. Yet a genuine, resampling-robust trend signal survives at the 4h and 1d timeframes, where the average turns slowly enough to filter noise. The idea is worth carrying forward only in a slower, fee-aware form — a longer average or a confirmation filter to cut the whipsaw, and fractional sizing to remove the compounding distortion — which would be a different study from the rule as written here.

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