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Enter Long When WMA 20 Comes Within 0.5% of Crossing Above WMA 50, Exit Within 0.5% of Crossing Back Below

An anticipatory take on the WMA 20/50 crossover: instead of waiting for the fast weighted moving average to cross the slow one, it acts half a percent early — entering as the fast average closes to within 0.5% of crossing up, and exiting as it falls back to within 0.5% of crossing down, stepping aside before the death cross confirms. 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.

---

• 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_FAST_PERIOD = 20
WMA_SLOW_PERIOD = 50

# Half-percent band around the crossover: the rule acts when the fast average
# comes within this fraction of the slow one, a step before the cross itself.
PROXIMITY_THRESHOLD = 0.5 / 100

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

# 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",
    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 the same two weighted moving averages of the close — a fast one over 20 bars and a slow one over 50 — but acts a step before they cross, not at the cross itself. Writing the gap between them as a fraction of the slow average,

$$g(t) = \frac{\mathrm{WMA}_{20}(t) - \mathrm{WMA}_{50}(t)}{\mathrm{WMA}_{50}(t)},$$

a long position opens as the fast average rises to within $\delta = 0.5\%$ below the slow one — the moment $g$ crosses up through $-\delta$ — and closes as it falls back to within $\delta$ above — the moment $g$ crosses down through $+\delta$:

$$\text{open at } t \iff g(t-1) \le -\delta \;\land\; g(t) > -\delta, \qquad \text{close at } t \iff g(t-1) \ge +\delta \;\land\; g(t) < +\delta.$$

Between the two rails the position simply holds. Entering at the lower rail gets long half a percent before the golden cross would confirm; exiting at the upper rail steps aside half a percent before the death cross, giving back less of an open profit when momentum turns. It is long-only and always fully in or fully out.

def strategy(prices, fast, slow, threshold):
    wma = {
        "fast": talib.WMA(prices, timeperiod=fast),
        "slow": talib.WMA(prices, timeperiod=slow),
    }

    # Signed gap between the averages, as a fraction of the slow one. The rule
    # fires on the band `threshold` wide on either side of the crossover.
    gap = (wma["fast"] - wma["slow"]) / wma["slow"]
    rising_into_band = (gap[:-1] <= -threshold) & (gap[1:] > -threshold)
    falling_into_band = (gap[:-1] >= threshold) & (gap[1:] < threshold)

    # +1 enters (anticipating the upward cross), -1 exits (anticipating the
    # downward cross), then hold the last state forward so the position
    # persists between crossings.
    signal = np.zeros(len(prices))
    signal[1:][rising_into_band] = 1
    signal[1:][falling_into_band] = -1
    held = pd.Series(signal).replace(0, np.nan).ffill().fillna(0).to_numpy()
    in_position = (held > 0).astype(np.int8)

    change = np.diff(in_position)
    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 anticipatory entries (green) and exits (red) can be read against the two averages — each firing a step before the lines actually cross.

rng = np.random.default_rng(0)
example_prices = 100 * np.cumprod(1 + rng.normal(0, 0.01, size=300))
example_wma, example_opens, example_closes = strategy(
    prices=example_prices,
    fast=WMA_FAST_PERIOD,
    slow=WMA_SLOW_PERIOD,
    threshold=PROXIMITY_THRESHOLD,
)

# 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["fast"], mode="lines", name="WMA 20", line=dict(color="#00d4aa", width=1)
    )
)
fig.add_trace(
    go.Scatter(
        y=example_wma["slow"], mode="lines", name="WMA 50", 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 Averages — the fast and slow weighted moving averages (WMA 20 and WMA 50) from which the signals are derived.
• Entries — bars where a position is opened.
• Exits — bars where a position is closed.
• Trade P&L % — per-trade net return, $\text{pnl}_i = (r_i - 1)\cdot 100\%$ with $r_i = (1 - f)^2\,p^{\text{exit}}_i / p^{\text{entry}}_i$ and $f$ the per-side fee.
• Cumulative Win Rate % — running share of winning trades, $\frac{1}{n}\sum_{i=1}^{n}\mathbf{1}\{\text{pnl}_i > 0\}\cdot 100\%$.
• Cumulative P&L % — the running sum of per-trade P&L.
• 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 net per-trade returns computed to-date after each trade, $S_n = \frac{\bar x}{\operatorname{std}(x)}\sqrt{T_\text{year}}$ over $x_1,\dots,x_n$, with $x_i = r_i - 1$ and $T_\text{year}$ the observed number of trades per year.

def analytics(symbol, prices, bars_per_year):
    wma, opens, closes = strategy(
        prices=prices,
        fast=WMA_FAST_PERIOD,
        slow=WMA_SLOW_PERIOD,
        threshold=PROXIMITY_THRESHOLD,
    )

    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
    pnl_pct = (factor - 1) * 100

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

    # Rolling (to-date) Sharpe after each trade: per-trade fractional returns,
    # annualized by the observed trade frequency.
    returns = factor - 1
    with np.errstate(invalid="ignore", divide="ignore"):
        cum_mean = np.cumsum(returns) / np.arange(1, total_trades + 1)
        cum_var = (np.cumsum(returns**2) / np.arange(1, total_trades + 1)) - cum_mean**2
        cum_std = np.sqrt(np.clip(cum_var, 0, None))
        cum_sharpe_per_trade = np.where(cum_std > 0, cum_mean / cum_std, 0.0)
    cum_sharpe = cum_sharpe_per_trade * np.sqrt(
        np.arange(1, total_trades + 1) * bars_per_year / max(len(prices), 1)
    )

    return {
        "symbol": symbol,
        "prices": prices,
        "wma": wma,
        "opens": opens,
        "closes": closes,
        "pnl_pct": pnl_pct,
        "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.

def charts(results):
    symbols = list(dict.fromkeys(symbol for symbol, _ in results))
    metrics = [
        ("Price Change %", "pct_prices", None),
        ("Trade P&L %", "pnl_pct", 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
                fig.add_trace(
                    go.Scatter(
                        x=x_values,
                        y=y_values,
                        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:
                    fig.add_trace(
                        go.Scatter(
                            x=result["opens"] + offset,
                            y=result[gross_key],
                            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	56.1	-40.4	26.83	35.92	15.92	-0.1
BTCUSDT	1h	1,331.8	51.4	56.6	66.92	81.6	19.05	0.14
BTCUSDT	4h	1,318	45.6	370.2	1,020.67	1,096.88	44.6	0.66
BTCUSDT	1d	1,339.4	42.4	446.6	717.94	727.48	4.08	0.51
ETHUSDT	30m	444.3	57.9	186.5	151.63	221.21	68.57	0.37
ETHUSDT	1h	440.1	50.7	104.7	38.96	49.57	22.31	0.18
ETHUSDT	4h	429.1	39.4	280.3	286.86	311.12	16.15	0.48
ETHUSDT	1d	439.5	48.5	578.4	795.91	806.48	6.28	0.53
SOLUSDT	30m	1,957.1	49.5	268.8	255.79	350.85	175.41	0.59
SOLUSDT	1h	2,086.3	45.1	153.5	86.97	104.59	44.93	0.34
SOLUSDT	4h	2,107.4	38.8	681.7	3,629.51	3,821.73	182.07	0.9
SOLUSDT	1d	1,856.3	52.4	2,219	42,317.55	42,674.55	171.49	0.66
BNBUSDT	30m	34,845.9	55.5	604.9	4,452.3	6,310.76	1,026.48	0.87
BNBUSDT	1h	34,877.6	50.3	474.5	1,206.75	1,520.06	218.67	0.69
BNBUSDT	4h	34,864.7	46	528.2	4,396.88	4,738.42	195.92	0.94
BNBUSDT	1d	37,733.2	46.7	647.9	1,908.71	1,931.76	12.2	0.48
DOGEUSDT	30m	2,106.6	52.9	557.1	371.01	505.86	303.98	0.48
DOGEUSDT	1h	2,041.5	44.3	538.5	346.11	422.92	142.44	0.53
DOGEUSDT	4h	2,148.9	38.9	744.5	3,306.85	3,521.21	156.2	0.75
DOGEUSDT	1d	2,071.8	46.2	1,980.9	2,608.02	2,635.28	13.81	0.42
ALL	30m	1,511.3	57.9	414	1,751.13	2,272.06	591.34	0.99
ALL	1h	1,540.6	47.2	-28.1	32.84	38.68	17.29	-0.09
ALL	4h	1,544.9	43.7	539.9	1,165.77	1,226.03	71.07	0.71
ALL	1d	1,539.7	40	109.9	190.37	191.9	1.31	0.43

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	59.7	69.4	134.3	161.24	20.97	0.33
BTCUSDT	1h	2,336.9	52.4	79.5	149.51	169.04	18.56	0.37
BTCUSDT	4h	2,348.9	50	242.8	685.68	717.67	18.81	0.97
BTCUSDT	1d	2,340.7	58.8	411.2	1,548.02	1,558.58	4.25	0.89
ETHUSDT	30m	832	57.5	190.6	264.92	334.5	50.27	0.58
ETHUSDT	1h	825	52.8	81.8	79.81	92.8	26.57	0.23
ETHUSDT	4h	833.2	46.4	214.6	378.25	397.65	13.5	0.67
ETHUSDT	1d	779.2	52.6	741.8	2,883.46	2,905.46	9.51	0.58
SOLUSDT	30m	15,210.4	50.9	519.7	3,544.19	4,882.92	446.45	1.16
SOLUSDT	1h	15,289	45.1	51.6	27.13	32.65	18.94	0.11
SOLUSDT	4h	15,463.4	37.6	905.7	10,410.08	10,943.87	150.4	1.02
SOLUSDT	1d	15,594.7	61.9	2,728.5	173,617.01	175,081.68	590.6	0.71
BNBUSDT	30m	5,780.4	57.2	322.8	741.79	916.98	121.45	0.72
BNBUSDT	1h	5,761.4	52.4	270	525.74	602.34	76.23	0.68
BNBUSDT	4h	5,540.5	41	114.4	192.99	202.64	8.47	0.45
BNBUSDT	1d	5,641.4	50	71.6	151.39	152.73	1.45	0.35
DOGEUSDT	30m	15,851.6	52	841.1	1,419.22	1,849.53	505.71	0.62
DOGEUSDT	1h	15,731.9	47.3	429.4	515.99	614.8	128.58	0.63
DOGEUSDT	4h	15,847.8	31.4	903	2,497.6	2,638.3	100.13	0.67
DOGEUSDT	1d	13,838.4	55.6	736.5	1,629.15	1,640.93	6.99	0.53
ALL	30m	8,002.9	56.6	483.5	1,602.67	2,063.69	274.19	0.75
ALL	1h	7,988.8	49	123.2	100.82	118.69	35.46	0.31
ALL	4h	8,006.8	41.4	638.9	1,757.8	1,853.86	90.4	0.67
ALL	1d	7,638.9	72.2	4,068.1	67,867.91	68,358.37	212.13	0.49

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	90.2	57.8	14.8
BTCUSDT	1h	0	12.7	0	78.5	70	18.7
BTCUSDT	4h	38.1	41.3	67	64	88.4	51
BTCUSDT	1d	24.9	33	56.9	56	97.8	42.5
ETHUSDT	30m	3.9	25.4	12	94.8	47.6	25
ETHUSDT	1h	0	14.9	0	76.8	64.3	18.6
ETHUSDT	4h	27.9	31.4	30.4	48.5	87	37.3
ETHUSDT	1d	64	34.2	59.9	71.2	97.8	58.5
SOLUSDT	30m	0	37.4	27.1	73.8	54.8	28.2
SOLUSDT	1h	0	23.7	0	62.8	71.9	20.6
SOLUSDT	4h	67.9	54.5	100	47	91.6	69
SOLUSDT	1d	100	41.3	100	81	98.6	80.4
BNBUSDT	30m	0	52.9	100	88.8	50.9	44.8
BNBUSDT	1h	0	42.9	71.9	75.8	65.6	37.8
BNBUSDT	4h	0	56.7	100	65	88	47.3
BNBUSDT	1d	0	31.4	85.1	66.8	98	38.7
DOGEUSDT	30m	0	31.4	37.8	82.2	55.6	28.9
DOGEUSDT	1h	0	34.2	35.8	60.7	69.7	28.7
DOGEUSDT	4h	63.9	46.2	100	47.2	89.9	65
DOGEUSDT	1d	56.6	28.1	94.1	65.5	98.3	58.7
ALL	30m	53	59.5	82.6	94.8	61.8	64.4
ALL	1h	0	0	0	68	74.8	14.3
ALL	4h	37.6	44	70.9	59.3	91.8	52.1
ALL	1d	0	28.6	18.6	50	98.7	26.2

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 (roughly eight years for BTC and ETH, less for the others), anticipating the WMA 20/50 cross by half a percent is profitable after fees in 19 of 24 symbol × timeframe cells. The five exceptions cluster at one speed: four are the 1-hour timeframe — BTC (66.92), ETH (38.96), SOL (86.97) and the basket (32.84) all end below their starting capital — joined by BTC at 30m (26.83). The hourly bar is where the half-percent band misfires most.

Against buy-and-hold it wins only 6 of 24 cells. Equity Net clears the buy-and-hold result implied by Price Change % at ETH 1d, SOL 4h and 1d, DOGE 4h and 1d, and the smoothed basket at 30m — and nowhere else. In markets that trended as hard as these, acting early costs more than it saves: entering half a percent before the upward cross opens marginal positions that never confirm, and exiting half a percent before the downward cross hands back the top of every trend. Both edits trade away upside the cross itself would have kept.

Risk-adjusted, the edge is modest and uneven. Rolling Sharpe peaks on the smoothed basket at 30m (0.99) and across the 4-hour row (0.75–0.94), but sinks to nil where the whipsaws land — BTC 30m (−0.10), the basket 1h (−0.09), BTC 1h (0.14), ETH 1h (0.18). The composite score tells the same story: the strongest cells are slow-timeframe trenders (SOL 1d 80, SOL 4h 69, DOGE 4h 65) where a half-percent band barely matters, while every one of the five weakest cells is an hourly one (14–21). The overall grade is 40 / 100 — what edge exists sits exactly where the anticipation is least relevant.

Fees bite hardest at speed, and the early entries make it worse. At 30m the basket pays 591 in cumulative fees and BNB pays 1,026; the gross-to-net gap is widest on the fast timeframes (SOL 30m 350.85 gross against 255.79 net), precisely where the band adds the most marginal round-trips. Cumulative Win Rate % sits between 39% and 58% — the early exit books many small winners, but those small wins are also what cap the upside.

On resampled history the profitability repeats, but the advantage over holding does not. With the market's own behavior replayed in a new order, 22 of 24 cells stay profitable and the Rolling Sharpe ladder holds (0.33–1.16 at 30m, 0.45–1.02 at 4h, fading toward the daily), so the rule is reading real market structure rather than one chronology. Yet it beats buy-and-hold in just 3 of 24 cells — weaker still than on the real data. The part that survives is the absolute profit; the part that does not is any reason to prefer this over simply holding.

Caveats. Results come from one historical window with fixed (20, 50) lengths and a fixed 0.5% band; the basket is five liquid survivors, so survivorship flatters the absolute figures (and ALL inherits it); the strategy is long-only over a window in which every symbol rose; the resampled run is a single draw, not a distribution; and prices are spot klines while the fee models the futures taker rate.

Call: shelve. The half-percent anticipation does not earn its keep. It is broadly profitable, but it beats buy-and-hold in only a quarter of cells, its Sharpe is thin and turns negative on the fast timeframes the band was meant to help, and its one advantage over holding all but vanishes on a resampled history. What profit remains lives in slow-timeframe trends where the band is incidental — riding the confirmed cross is the better bet than anticipating it.

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