mirror of
https://github.com/OpenByteInc/QuantDinger.git
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597 lines
36 KiB
SQL
597 lines
36 KiB
SQL
-- ===== Strategy API V2 canonical template seed =====
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DELETE FROM qd_script_templates
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WHERE template_key NOT IN (
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'strategy_v2_single_ma',
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'strategy_v2_double_ma',
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'strategy_v2_bullish_three_lines',
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'strategy_v2_bullish_three_lines_trend',
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'strategy_v2_turtle',
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'strategy_v2_indicator_resonance',
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'strategy_v2_macd_kdj',
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'strategy_v2_supertrend',
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'strategy_v2_market_cap_barbell',
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'strategy_v2_momentum_top_n',
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'strategy_v2_low_volatility',
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'strategy_v2_quality_growth'
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);
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INSERT INTO qd_script_templates
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(template_key, asset_type, title, description, code, param_schema, tags, icon, accent, sort_order, is_active, metadata, updated_at)
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VALUES
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('strategy_v2_single_ma', 'script', 'Single Moving Average', 'A parameterized SPY trend strategy using one moving average.', $single$"""
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Single Moving Average
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SPY trend regime driven by a configurable moving average.
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"""
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# @param ma_period int 50 range=2:250:1
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# @param target_pct float 0.95 range=0.05:1:0.05
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def initialize(context):
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g.symbol = "USStock:SPY"
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context.set_universe([g.symbol])
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context.set_benchmark(g.symbol)
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context.subscribe(frequency="1d")
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context.set_warmup(260)
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def handle_data(context, data):
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ma_period = int(context.params.get("ma_period", 50))
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target_pct = float(context.params.get("target_pct", 0.95))
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bars = get_history(ma_period + 2, "1d", "close", g.symbol)
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if len(bars) < ma_period + 1:
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return
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close = bars["close"]
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average = float(close.iloc[:-1].tail(ma_period).mean())
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price = float(close.iloc[-1])
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position = get_position(g.symbol)
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is_long = float(position.amount or 0.0) > 0
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if price > average and not is_long:
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order_target_percent(g.symbol, target_pct, reason="single_ma_entry")
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elif price <= average and is_long:
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order_target_percent(g.symbol, 0.0, reason="single_ma_exit")
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$single$, '{"params":[{"name":"ma_period","type":"integer","default":50,"min":2,"max":250,"step":1,"labelKey":"strategyV2.params.maPeriod","descriptionKey":"strategyV2.params.maPeriodDesc"},{"name":"target_pct","type":"percent","default":0.95,"min":0.05,"max":1,"step":0.05,"labelKey":"strategyV2.params.targetPosition","descriptionKey":"strategyV2.params.targetPositionDesc"}]}'::jsonb, '["strategy-v2","cta","moving-average","us-stock"]'::jsonb, 'line-chart', 'green', 10, TRUE, '{"source":"system_seed","version":8,"apiVersion":2}'::jsonb, NOW()),
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('strategy_v2_double_ma', 'script', 'Dual Moving Average', 'A parameterized BTC perpetual dual moving-average strategy with optional leverage.', $double$"""
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Dual Moving Average
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BTC perpetual trend strategy with configurable long and short regimes.
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"""
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# @param fast_period int 20 range=2:100:1
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# @param slow_period int 60 range=5:300:1
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# @param target_pct float 0.95 range=0.05:1:0.05
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# @param allow_short bool true
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def initialize(context):
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g.symbol = "Crypto:BTC/USDT@swap"
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context.set_universe([g.symbol])
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context.set_benchmark("Crypto:BTC/USDT@spot")
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context.subscribe(frequency="4h")
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context.set_warmup(310)
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context.allow_leverage(max_leverage=20)
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def handle_data(context, data):
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fast_period = int(context.params.get("fast_period", 20))
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slow_period = int(context.params.get("slow_period", 60))
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target_pct = float(context.params.get("target_pct", 0.95))
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allow_short = bool(context.params.get("allow_short", True))
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if fast_period >= slow_period:
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return
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bars = get_history(slow_period + 2, "4h", "close", g.symbol)
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if len(bars) < slow_period + 1:
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return
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close = bars["close"]
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fast = float(close.tail(fast_period).mean())
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slow = float(close.tail(slow_period).mean())
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position = get_position(g.symbol)
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amount = float(position.amount or 0.0)
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target = target_pct if fast > slow else (-target_pct if allow_short else 0.0)
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if (target > 0 and amount <= 0) or (target < 0 and amount >= 0) or (target == 0 and amount != 0):
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order_target_percent(g.symbol, target, reason="dual_ma_regime_change")
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$double$, '{"params":[{"name":"fast_period","type":"integer","default":20,"min":2,"max":100,"step":1,"labelKey":"trading-assistant.templateParam.fast_period.label","descriptionKey":"trading-assistant.templateParam.fast_period.desc"},{"name":"slow_period","type":"integer","default":60,"min":5,"max":300,"step":1,"labelKey":"trading-assistant.templateParam.slow_period.label","descriptionKey":"trading-assistant.templateParam.slow_period.desc"},{"name":"target_pct","type":"percent","default":0.95,"min":0.05,"max":1,"step":0.05,"labelKey":"strategyV2.params.targetPosition","descriptionKey":"strategyV2.params.targetPositionDesc"},{"name":"allow_short","type":"boolean","default":true,"labelKey":"strategyV2.params.allowShort","descriptionKey":"strategyV2.params.allowShortDesc"}]}'::jsonb, '["strategy-v2","cta","moving-average","crypto","swap"]'::jsonb, 'swap', 'blue', 20, TRUE, '{"source":"system_seed","version":8,"apiVersion":2}'::jsonb, NOW()),
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('strategy_v2_bullish_three_lines', 'script', 'Bullish Candle Through Three Averages', 'An A-share bullish candle breakout through three configurable averages.', $three$"""
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Bullish Candle Through Three Averages
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Daily A-share breakout through three configurable moving averages.
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"""
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# @param short_period int 5 range=2:60:1
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# @param mid_period int 10 range=3:120:1
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# @param long_period int 20 range=5:250:1
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# @param min_body_pct float 0.02 range=0:0.2:0.005
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# @param target_pct float 0.95 range=0.05:1:0.05
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def initialize(context):
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g.symbol = "CNStock:600519.SH"
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context.set_universe([g.symbol])
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context.set_benchmark(g.symbol)
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context.subscribe(frequency="1d")
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context.set_warmup(260)
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def handle_data(context, data):
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periods = [
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int(context.params.get("short_period", 5)),
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int(context.params.get("mid_period", 10)),
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int(context.params.get("long_period", 20)),
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]
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min_body_pct = float(context.params.get("min_body_pct", 0.02))
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target_pct = float(context.params.get("target_pct", 0.95))
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if not periods[0] < periods[1] < periods[2]:
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return
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bars = get_history(periods[-1] + 3, "1d", ["open", "close"], g.symbol)
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if len(bars) < periods[-1] + 1:
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return
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close = bars["close"]
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current = bars.iloc[-1]
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averages = [float(close.iloc[:-1].tail(period).mean()) for period in periods]
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open_price = float(current["open"])
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close_price = float(current["close"])
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body_pct = (close_price - open_price) / open_price if open_price > 0 else 0.0
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crossed = body_pct >= min_body_pct and open_price <= min(averages) and close_price >= max(averages)
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position = get_position(g.symbol)
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is_long = float(position.amount or 0.0) > 0
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if crossed and not is_long:
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order_target_percent(g.symbol, target_pct, reason="bullish_three_lines_entry")
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elif is_long and close_price < averages[-1]:
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order_target_percent(g.symbol, 0.0, reason="bullish_three_lines_exit")
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$three$, '{"params":[{"name":"short_period","type":"integer","default":5,"min":2,"max":60,"step":1,"labelKey":"strategyV2.params.shortPeriod","descriptionKey":"strategyV2.params.shortPeriodDesc"},{"name":"mid_period","type":"integer","default":10,"min":3,"max":120,"step":1,"labelKey":"strategyV2.params.midPeriod","descriptionKey":"strategyV2.params.midPeriodDesc"},{"name":"long_period","type":"integer","default":20,"min":5,"max":250,"step":1,"labelKey":"strategyV2.params.longPeriod","descriptionKey":"strategyV2.params.longPeriodDesc"},{"name":"min_body_pct","type":"percent","default":0.02,"min":0,"max":0.2,"step":0.005,"labelKey":"strategyV2.params.minBodyPct","descriptionKey":"strategyV2.params.minBodyPctDesc"},{"name":"target_pct","type":"percent","default":0.95,"min":0.05,"max":1,"step":0.05,"labelKey":"strategyV2.params.targetPosition","descriptionKey":"strategyV2.params.targetPositionDesc"}]}'::jsonb, '["strategy-v2","cta","candlestick","a-share"]'::jsonb, 'rise', 'red', 30, TRUE, '{"source":"system_seed","version":8,"apiVersion":2}'::jsonb, NOW()),
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('strategy_v2_bullish_three_lines_trend', 'script', 'Bullish Three Averages With Trend Filter', 'The three-average breakout combined with a configurable rising trend filter.', $threetrend$"""
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Bullish Three Averages With Trend Filter
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Daily A-share breakout with a configurable rising trend filter.
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"""
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# @param short_period int 5 range=2:60:1
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# @param mid_period int 10 range=3:120:1
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# @param long_period int 20 range=5:250:1
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# @param trend_period int 60 range=20:300:1
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# @param trend_slope_bars int 5 range=1:30:1
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# @param min_body_pct float 0.02 range=0:0.2:0.005
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# @param target_pct float 0.95 range=0.05:1:0.05
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def initialize(context):
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g.symbol = "CNStock:600519.SH"
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context.set_universe([g.symbol])
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context.set_benchmark(g.symbol)
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context.subscribe(frequency="1d")
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context.set_warmup(340)
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def handle_data(context, data):
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periods = [int(context.params.get("short_period", 5)), int(context.params.get("mid_period", 10)), int(context.params.get("long_period", 20))]
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trend_period = int(context.params.get("trend_period", 60))
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slope_bars = int(context.params.get("trend_slope_bars", 5))
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min_body_pct = float(context.params.get("min_body_pct", 0.02))
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target_pct = float(context.params.get("target_pct", 0.95))
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required = max(periods[-1], trend_period) + slope_bars + 2
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bars = get_history(required, "1d", ["open", "close"], g.symbol)
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if len(bars) < required - 1 or not periods[0] < periods[1] < periods[2]:
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return
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close = bars["close"]
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current = bars.iloc[-1]
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averages = [float(close.iloc[:-1].tail(period).mean()) for period in periods]
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trend_now = float(close.iloc[:-1].tail(trend_period).mean())
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trend_before = float(close.iloc[:-1 - slope_bars].tail(trend_period).mean())
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open_price = float(current["open"])
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close_price = float(current["close"])
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body_pct = (close_price - open_price) / open_price if open_price > 0 else 0.0
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crossed = body_pct >= min_body_pct and open_price <= min(averages) and close_price >= max(averages)
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trend_ok = close_price > trend_now and trend_now > trend_before
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position = get_position(g.symbol)
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is_long = float(position.amount or 0.0) > 0
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if crossed and trend_ok and not is_long:
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order_target_percent(g.symbol, target_pct, reason="bullish_three_lines_trend_entry")
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elif is_long and (close_price < averages[-1] or not trend_ok):
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order_target_percent(g.symbol, 0.0, reason="bullish_three_lines_trend_exit")
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$threetrend$, '{"params":[{"name":"short_period","type":"integer","default":5,"min":2,"max":60,"step":1,"labelKey":"strategyV2.params.shortPeriod"},{"name":"mid_period","type":"integer","default":10,"min":3,"max":120,"step":1,"labelKey":"strategyV2.params.midPeriod"},{"name":"long_period","type":"integer","default":20,"min":5,"max":250,"step":1,"labelKey":"strategyV2.params.longPeriod"},{"name":"trend_period","type":"integer","default":60,"min":20,"max":300,"step":1,"labelKey":"strategyV2.params.trendPeriod"},{"name":"trend_slope_bars","type":"integer","default":5,"min":1,"max":30,"step":1,"labelKey":"strategyV2.params.trendSlopeBars"},{"name":"min_body_pct","type":"percent","default":0.02,"min":0,"max":0.2,"step":0.005,"labelKey":"strategyV2.params.minBodyPct"},{"name":"target_pct","type":"percent","default":0.95,"min":0.05,"max":1,"step":0.05,"labelKey":"strategyV2.params.targetPosition"}]}'::jsonb, '["strategy-v2","cta","candlestick","trend","a-share"]'::jsonb, 'area-chart', 'orange', 40, TRUE, '{"source":"system_seed","version":8,"apiVersion":2}'::jsonb, NOW()),
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('strategy_v2_turtle', 'script', 'Turtle Trading', 'A configurable Donchian breakout, channel exit, and ATR stop strategy on SPY.', $turtle$"""
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Turtle Trading
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Configurable Donchian breakout, channel exit, and ATR risk stop.
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"""
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# @param entry_period int 20 range=5:120:1
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# @param exit_period int 10 range=2:60:1
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# @param atr_period int 14 range=2:100:1
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# @param atr_stop_mult float 2 range=0.5:10:0.25
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# @param target_pct float 0.95 range=0.05:1:0.05
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def initialize(context):
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g.symbol = "USStock:SPY"
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g.entry_price = None
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context.set_universe([g.symbol])
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context.set_benchmark(g.symbol)
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context.subscribe(frequency="1d")
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context.set_warmup(140)
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def handle_data(context, data):
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entry_period = int(context.params.get("entry_period", 20))
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exit_period = int(context.params.get("exit_period", 10))
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atr_period = int(context.params.get("atr_period", 14))
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atr_stop_mult = float(context.params.get("atr_stop_mult", 2.0))
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target_pct = float(context.params.get("target_pct", 0.95))
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required = max(entry_period, exit_period, atr_period) + 2
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bars = get_history(required, "1d", ["high", "low", "close"], g.symbol)
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atr = indicator("ATR", g.symbol, timeperiod=atr_period)
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if len(bars) < required - 1 or len(atr) < 2:
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return
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close = float(bars["close"].iloc[-1])
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entry_high = float(bars["high"].iloc[-entry_period - 1:-1].max())
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exit_low = float(bars["low"].iloc[-exit_period - 1:-1].min())
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atr_value = float(atr.iloc[-1])
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position = get_position(g.symbol)
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is_long = float(position.amount or 0.0) > 0
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if not is_long and close > entry_high:
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g.entry_price = close
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order_target_percent(g.symbol, target_pct, reason="turtle_breakout")
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elif is_long:
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stop_price = float(g.entry_price or close) - atr_stop_mult * atr_value
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if close < exit_low or close < stop_price:
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order_target_percent(g.symbol, 0.0, reason="turtle_exit")
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g.entry_price = None
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$turtle$, '{"params":[{"name":"entry_period","type":"integer","default":20,"min":5,"max":120,"step":1,"labelKey":"strategyV2.params.entryPeriod"},{"name":"exit_period","type":"integer","default":10,"min":2,"max":60,"step":1,"labelKey":"strategyV2.params.exitPeriod"},{"name":"atr_period","type":"integer","default":14,"min":2,"max":100,"step":1,"labelKey":"strategyV2.params.atrPeriod"},{"name":"atr_stop_mult","type":"number","default":2,"min":0.5,"max":10,"step":0.25,"labelKey":"strategyV2.params.atrStopMult"},{"name":"target_pct","type":"percent","default":0.95,"min":0.05,"max":1,"step":0.05,"labelKey":"strategyV2.params.targetPosition"}]}'::jsonb, '["strategy-v2","cta","breakout","turtle","us-stock"]'::jsonb, 'flag', 'cyan', 50, TRUE, '{"source":"system_seed","version":8,"apiVersion":2}'::jsonb, NOW()),
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('strategy_v2_indicator_resonance', 'script', 'Indicator Resonance', 'A parameterized QQQ strategy requiring MACD, RSI, and ADX confirmation.', $resonance$"""
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Indicator Resonance
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MACD, RSI, and ADX confirm the same bullish regime.
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"""
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# @param fast_period int 12 range=2:100:1
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# @param slow_period int 26 range=3:200:1
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# @param signal_period int 9 range=2:100:1
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# @param rsi_period int 14 range=2:100:1
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# @param rsi_min float 50 range=0:100:1
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# @param rsi_max float 75 range=0:100:1
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# @param adx_period int 14 range=2:100:1
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# @param adx_min float 20 range=0:100:1
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# @param target_pct float 0.95 range=0.05:1:0.05
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def initialize(context):
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g.symbol = "USStock:QQQ"
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context.set_universe([g.symbol])
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context.set_benchmark(g.symbol)
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context.subscribe(frequency="1d")
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context.set_warmup(210)
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def handle_data(context, data):
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fast_period = int(context.params.get("fast_period", 12))
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slow_period = int(context.params.get("slow_period", 26))
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signal_period = int(context.params.get("signal_period", 9))
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rsi_period = int(context.params.get("rsi_period", 14))
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rsi_min = float(context.params.get("rsi_min", 50))
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rsi_max = float(context.params.get("rsi_max", 75))
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adx_period = int(context.params.get("adx_period", 14))
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adx_min = float(context.params.get("adx_min", 20))
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target_pct = float(context.params.get("target_pct", 0.95))
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macd = indicator("MACD", g.symbol, fastperiod=fast_period, slowperiod=slow_period, signalperiod=signal_period)
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rsi = indicator("RSI", g.symbol, timeperiod=rsi_period)
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adx = indicator("ADX", g.symbol, timeperiod=adx_period)
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if len(macd) < 2 or len(rsi) < 2 or len(adx) < 2:
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return
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histogram = float(macd["macdhist"].iloc[-1])
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rsi_value = float(rsi.iloc[-1])
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adx_value = float(adx.iloc[-1])
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if not all(value == value for value in (histogram, rsi_value, adx_value)):
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return
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bullish = histogram > 0 and rsi_min < rsi_value < rsi_max and adx_value > adx_min
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position = get_position(g.symbol)
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is_long = float(position.amount or 0.0) > 0
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if bullish and not is_long:
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order_target_percent(g.symbol, target_pct, reason="indicator_resonance_entry")
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elif not bullish and is_long:
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order_target_percent(g.symbol, 0.0, reason="indicator_resonance_exit")
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$resonance$, '{"params":[{"name":"fast_period","type":"integer","default":12,"min":2,"max":100,"step":1,"labelKey":"trading-assistant.templateParam.fast_period.label"},{"name":"slow_period","type":"integer","default":26,"min":3,"max":200,"step":1,"labelKey":"trading-assistant.templateParam.slow_period.label"},{"name":"signal_period","type":"integer","default":9,"min":2,"max":100,"step":1,"labelKey":"strategyV2.params.signalPeriod"},{"name":"rsi_period","type":"integer","default":14,"min":2,"max":100,"step":1,"labelKey":"strategyV2.params.rsiPeriod"},{"name":"rsi_min","type":"number","default":50,"min":0,"max":100,"step":1,"labelKey":"strategyV2.params.rsiMin"},{"name":"rsi_max","type":"number","default":75,"min":0,"max":100,"step":1,"labelKey":"strategyV2.params.rsiMax"},{"name":"adx_period","type":"integer","default":14,"min":2,"max":100,"step":1,"labelKey":"strategyV2.params.adxPeriod"},{"name":"adx_min","type":"number","default":20,"min":0,"max":100,"step":1,"labelKey":"strategyV2.params.adxMin"},{"name":"target_pct","type":"percent","default":0.95,"min":0.05,"max":1,"step":0.05,"labelKey":"strategyV2.params.targetPosition"}]}'::jsonb, '["strategy-v2","cta","ta-lib","resonance","us-stock"]'::jsonb, 'fund', 'purple', 60, TRUE, '{"source":"system_seed","version":8,"apiVersion":2}'::jsonb, NOW()),
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('strategy_v2_macd_kdj', 'script', 'MACD and KDJ Confirmation', 'A BTC perpetual strategy combining MACD momentum, stochastic KDJ confirmation, and explicit position protection.', $macdkdj$"""
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MACD and KDJ Confirmation
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BTC perpetual momentum with state-transition entries confirmed by MACD and stochastic KDJ.
|
|
"""
|
|
|
|
# @param fast_period int 12 range=2:100:1
|
|
# @param slow_period int 26 range=3:200:1
|
|
# @param signal_period int 9 range=2:100:1
|
|
# @param kdj_period int 9 range=2:100:1
|
|
# @param kdj_smooth_k int 3 range=1:20:1
|
|
# @param kdj_smooth_d int 3 range=1:20:1
|
|
# @param overbought float 85 range=50:100:1
|
|
# @param target_pct float 4.75 range=0.1:5:0.05
|
|
# @param stop_loss_pct float 0.02 range=0.005:0.2:0.005
|
|
# @param trailing_activation_pct float 0.05 range=0.005:0.5:0.005
|
|
# @param trailing_stop_pct float 0.01 range=0.005:0.2:0.005
|
|
|
|
def initialize(context):
|
|
g.symbol = "Crypto:BTC/USDT@swap"
|
|
context.set_universe([g.symbol])
|
|
context.set_benchmark("Crypto:BTC/USDT@spot")
|
|
context.subscribe(frequency="4h")
|
|
context.set_warmup(210)
|
|
context.allow_leverage(max_leverage=5)
|
|
|
|
|
|
def handle_data(context, data):
|
|
fast_period = int(context.params.get("fast_period", 12))
|
|
slow_period = int(context.params.get("slow_period", 26))
|
|
signal_period = int(context.params.get("signal_period", 9))
|
|
kdj_period = int(context.params.get("kdj_period", 9))
|
|
kdj_smooth_k = int(context.params.get("kdj_smooth_k", 3))
|
|
kdj_smooth_d = int(context.params.get("kdj_smooth_d", 3))
|
|
overbought = float(context.params.get("overbought", 85))
|
|
target_pct = float(context.params.get("target_pct", 4.75))
|
|
stop_loss_pct = float(context.params.get("stop_loss_pct", 0.02))
|
|
trailing_activation_pct = float(context.params.get("trailing_activation_pct", 0.05))
|
|
trailing_stop_pct = float(context.params.get("trailing_stop_pct", 0.01))
|
|
macd = indicator("MACD", g.symbol, fastperiod=fast_period, slowperiod=slow_period, signalperiod=signal_period)
|
|
kdj = indicator("STOCH", g.symbol, fastk_period=kdj_period, slowk_period=kdj_smooth_k, slowd_period=kdj_smooth_d)
|
|
if len(macd) < 2 or len(kdj) < 2:
|
|
return
|
|
previous_histogram = float(macd["macdhist"].iloc[-2])
|
|
histogram = float(macd["macdhist"].iloc[-1])
|
|
previous_k = float(kdj["slowk"].iloc[-2])
|
|
previous_d = float(kdj["slowd"].iloc[-2])
|
|
k_value = float(kdj["slowk"].iloc[-1])
|
|
d_value = float(kdj["slowd"].iloc[-1])
|
|
if not all(value == value for value in (previous_histogram, histogram, previous_k, previous_d, k_value, d_value)):
|
|
return
|
|
macd_cross_up = previous_histogram <= 0 < histogram
|
|
kdj_cross_up = previous_k <= previous_d and k_value > d_value
|
|
enter = histogram > 0 and (macd_cross_up or kdj_cross_up) and k_value < overbought
|
|
exit_signal = histogram <= 0 or (previous_k >= previous_d and k_value < d_value)
|
|
position = get_position(g.symbol)
|
|
is_long = float(position.amount or 0.0) > 0
|
|
if enter and not is_long:
|
|
order_target_percent(
|
|
g.symbol,
|
|
target_pct,
|
|
reason="macd_kdj_entry",
|
|
stop_loss_pct=stop_loss_pct,
|
|
trailing_activation_pct=trailing_activation_pct,
|
|
trailing_stop_pct=trailing_stop_pct,
|
|
)
|
|
elif exit_signal and is_long:
|
|
order_target_percent(g.symbol, 0.0, reason="macd_kdj_exit")
|
|
$macdkdj$, '{"params":[{"name":"fast_period","type":"integer","default":12,"min":2,"max":100,"step":1,"labelKey":"trading-assistant.templateParam.fast_period.label"},{"name":"slow_period","type":"integer","default":26,"min":3,"max":200,"step":1,"labelKey":"trading-assistant.templateParam.slow_period.label"},{"name":"signal_period","type":"integer","default":9,"min":2,"max":100,"step":1,"labelKey":"strategyV2.params.signalPeriod"},{"name":"kdj_period","type":"integer","default":9,"min":2,"max":100,"step":1,"labelKey":"strategyV2.params.kdjPeriod"},{"name":"kdj_smooth_k","type":"integer","default":3,"min":1,"max":20,"step":1,"labelKey":"strategyV2.params.kdjSmoothK"},{"name":"kdj_smooth_d","type":"integer","default":3,"min":1,"max":20,"step":1,"labelKey":"strategyV2.params.kdjSmoothD"},{"name":"overbought","type":"number","default":85,"min":50,"max":100,"step":1,"labelKey":"trading-assistant.templateParam.overbought.label"},{"name":"target_pct","type":"number","default":4.75,"min":0.1,"max":5,"step":0.05,"labelKey":"strategyV2.params.targetExposure"},{"name":"stop_loss_pct","type":"percent","default":0.02,"min":0.005,"max":0.2,"step":0.005,"labelKey":"strategyV2.params.stopLoss"},{"name":"trailing_activation_pct","type":"percent","default":0.05,"min":0.005,"max":0.5,"step":0.005,"labelKey":"strategyV2.params.trailingActivation"},{"name":"trailing_stop_pct","type":"percent","default":0.01,"min":0.005,"max":0.2,"step":0.005,"labelKey":"strategyV2.params.trailingDrawdown"}]}'::jsonb, '["strategy-v2","cta","ta-lib","macd","kdj","crypto","swap","risk"]'::jsonb, 'bar-chart', 'gold', 70, TRUE, '{"source":"system_seed","version":9,"apiVersion":2}'::jsonb, NOW()),
|
|
|
|
('strategy_v2_supertrend', 'script', 'SuperTrend', 'A configurable SPY SuperTrend strategy using ATR trailing bands.', $supertrend$"""
|
|
SuperTrend
|
|
ATR trailing bands define a stateful SPY trend regime.
|
|
"""
|
|
|
|
# @param atr_period int 10 range=2:100:1
|
|
# @param atr_multiplier float 3 range=0.5:10:0.25
|
|
# @param target_pct float 0.95 range=0.05:1:0.05
|
|
|
|
def initialize(context):
|
|
g.symbol = "USStock:SPY"
|
|
g.trend = 0
|
|
g.upper_band = None
|
|
g.lower_band = None
|
|
context.set_universe([g.symbol])
|
|
context.set_benchmark(g.symbol)
|
|
context.subscribe(frequency="1d")
|
|
context.set_warmup(120)
|
|
|
|
|
|
def handle_data(context, data):
|
|
atr_period = int(context.params.get("atr_period", 10))
|
|
multiplier = float(context.params.get("atr_multiplier", 3.0))
|
|
target_pct = float(context.params.get("target_pct", 0.95))
|
|
bars = get_history(atr_period + 3, "1d", ["high", "low", "close"], g.symbol)
|
|
atr = indicator("ATR", g.symbol, timeperiod=atr_period)
|
|
if len(bars) < atr_period + 2 or len(atr) < 2:
|
|
return
|
|
high = float(bars["high"].iloc[-1])
|
|
low = float(bars["low"].iloc[-1])
|
|
close = float(bars["close"].iloc[-1])
|
|
previous_close = float(bars["close"].iloc[-2])
|
|
atr_value = float(atr.iloc[-1])
|
|
middle = (high + low) / 2.0
|
|
basic_upper = middle + multiplier * atr_value
|
|
basic_lower = middle - multiplier * atr_value
|
|
previous_upper = float(g.upper_band) if g.upper_band is not None else basic_upper
|
|
previous_lower = float(g.lower_band) if g.lower_band is not None else basic_lower
|
|
g.upper_band = basic_upper if basic_upper < previous_upper or previous_close > previous_upper else previous_upper
|
|
g.lower_band = basic_lower if basic_lower > previous_lower or previous_close < previous_lower else previous_lower
|
|
if close > previous_upper:
|
|
g.trend = 1
|
|
elif close < previous_lower:
|
|
g.trend = -1
|
|
position = get_position(g.symbol)
|
|
is_long = float(position.amount or 0.0) > 0
|
|
if g.trend > 0 and not is_long:
|
|
order_target_percent(g.symbol, target_pct, reason="supertrend_entry")
|
|
elif g.trend < 0 and is_long:
|
|
order_target_percent(g.symbol, 0.0, reason="supertrend_exit")
|
|
$supertrend$, '{"params":[{"name":"atr_period","type":"integer","default":10,"min":2,"max":100,"step":1,"labelKey":"strategyV2.params.atrPeriod"},{"name":"atr_multiplier","type":"number","default":3,"min":0.5,"max":10,"step":0.25,"labelKey":"strategyV2.params.atrMultiplier"},{"name":"target_pct","type":"percent","default":0.95,"min":0.05,"max":1,"step":0.05,"labelKey":"strategyV2.params.targetPosition"}]}'::jsonb, '["strategy-v2","cta","supertrend","atr","us-stock"]'::jsonb, 'stock', 'lime', 80, TRUE, '{"source":"system_seed","version":8,"apiVersion":2}'::jsonb, NOW()),
|
|
|
|
('strategy_v2_market_cap_barbell', 'portfolio_strategy', 'Small and Large Cap Barbell', 'A weekly cross-sectional portfolio combining small and large eligible U.S. companies.', $marketcap$"""
|
|
Small and Large Cap Barbell
|
|
Weekly point-in-time market-cap barbell with a profitability filter.
|
|
"""
|
|
|
|
# @param per_side int 3 range=1:6:1
|
|
# @param min_roe float 0 range=-1:1:0.01
|
|
# @param max_weight float 0.2 range=0.05:1:0.05
|
|
|
|
def initialize(context):
|
|
g.universe = [
|
|
"USStock:AAPL", "USStock:MSFT", "USStock:NVDA", "USStock:AMZN", "USStock:META",
|
|
"USStock:GOOGL", "USStock:AVGO", "USStock:COST", "USStock:JPM", "USStock:XOM",
|
|
]
|
|
context.set_universe(g.universe)
|
|
context.set_benchmark("USStock:SPY")
|
|
context.subscribe(frequency="1d")
|
|
context.set_warmup(10)
|
|
run_weekly(rebalance, weekday=1, time="09:35")
|
|
|
|
|
|
def rebalance(context, data):
|
|
per_side = int(context.params.get("per_side", 3))
|
|
min_roe = float(context.params.get("min_roe", 0.0))
|
|
max_weight = float(context.params.get("max_weight", 0.2))
|
|
symbols = list(g.universe)
|
|
fundamentals = get_fundamentals(["MARKET_CAP", "ROE"], symbols)
|
|
if fundamentals.empty:
|
|
return
|
|
eligible = fundamentals.dropna(subset=["MARKET_CAP"])
|
|
if "ROE" in eligible.columns:
|
|
eligible = eligible[(eligible["ROE"].isna()) | (eligible["ROE"] >= min_roe)]
|
|
ranking = eligible.sort_values("MARKET_CAP")
|
|
selected = list(dict.fromkeys(list(ranking.head(per_side).index) + list(ranking.tail(per_side).index)))
|
|
for symbol in get_positions().keys():
|
|
if symbol not in selected:
|
|
order_target_percent(symbol, 0.0, reason="market_cap_removed")
|
|
weight = min(max_weight, 1.0 / len(selected)) if selected else 0.0
|
|
for symbol in selected:
|
|
order_target_percent(symbol, weight, reason="market_cap_barbell")
|
|
$marketcap$, '{"params":[{"name":"per_side","type":"integer","default":3,"min":1,"max":6,"step":1,"labelKey":"strategyV2.params.perSide"},{"name":"min_roe","type":"number","default":0,"min":-1,"max":1,"step":0.01,"labelKey":"strategyV2.params.minRoe"},{"name":"max_weight","type":"percent","default":0.2,"min":0.05,"max":1,"step":0.05,"labelKey":"strategyV2.params.maxWeight"}]}'::jsonb, '["strategy-v2","portfolio","cross-sectional","fundamental","market-cap"]'::jsonb, 'appstore', 'geekblue', 110, TRUE, '{"source":"system_seed","version":8,"apiVersion":2}'::jsonb, NOW()),
|
|
|
|
('strategy_v2_momentum_top_n', 'portfolio_strategy', 'Momentum Top-N Rotation', 'A weekly U.S. stock portfolio selecting the strongest trailing momentum.', $momentum$"""
|
|
Momentum Top-N Rotation
|
|
Weekly cross-sectional rotation into the strongest trailing momentum names.
|
|
"""
|
|
|
|
# @param lookback int 60 range=10:250:5
|
|
# @param top_n int 4 range=1:10:1
|
|
# @param max_weight float 0.25 range=0.05:1:0.05
|
|
|
|
def initialize(context):
|
|
g.universe = [
|
|
"USStock:AAPL", "USStock:MSFT", "USStock:NVDA", "USStock:AMZN", "USStock:META",
|
|
"USStock:GOOGL", "USStock:AVGO", "USStock:COST", "USStock:JPM", "USStock:XOM",
|
|
]
|
|
context.set_universe(g.universe)
|
|
context.set_benchmark("USStock:SPY")
|
|
context.subscribe(frequency="1d")
|
|
context.set_warmup(260)
|
|
run_weekly(rebalance, weekday=1, time="09:35")
|
|
|
|
|
|
def rebalance(context, data):
|
|
lookback = int(context.params.get("lookback", 60))
|
|
top_n = int(context.params.get("top_n", 4))
|
|
max_weight = float(context.params.get("max_weight", 0.25))
|
|
scores = {}
|
|
for symbol in g.universe:
|
|
bars = get_history(lookback + 1, "1d", "close", symbol)
|
|
if len(bars) < lookback + 1:
|
|
continue
|
|
first = float(bars["close"].iloc[0])
|
|
last = float(bars["close"].iloc[-1])
|
|
if first > 0:
|
|
scores[symbol] = last / first - 1.0
|
|
selected = [symbol for symbol, score in sorted(scores.items(), key=lambda item: item[1], reverse=True)[:top_n] if score > 0]
|
|
for symbol in get_positions().keys():
|
|
if symbol not in selected:
|
|
order_target_percent(symbol, 0.0, reason="momentum_removed")
|
|
weight = min(max_weight, 1.0 / len(selected)) if selected else 0.0
|
|
for symbol in selected:
|
|
order_target_percent(symbol, weight, reason="momentum_top_n")
|
|
$momentum$, '{"params":[{"name":"lookback","type":"integer","default":60,"min":10,"max":250,"step":5,"labelKey":"strategyV2.params.lookback"},{"name":"top_n","type":"integer","default":4,"min":1,"max":10,"step":1,"labelKey":"strategyV2.params.topN"},{"name":"max_weight","type":"percent","default":0.25,"min":0.05,"max":1,"step":0.05,"labelKey":"strategyV2.params.maxWeight"}]}'::jsonb, '["strategy-v2","portfolio","cross-sectional","momentum","rotation"]'::jsonb, 'rocket', 'blue', 120, TRUE, '{"source":"system_seed","version":8,"apiVersion":2}'::jsonb, NOW()),
|
|
|
|
('strategy_v2_low_volatility', 'portfolio_strategy', 'Low Volatility Rotation', 'A weekly U.S. stock portfolio selecting the lowest realized volatility names.', $lowvol$"""
|
|
Low Volatility Rotation
|
|
Weekly cross-sectional rotation into the lowest realized volatility names.
|
|
"""
|
|
|
|
# @param lookback int 60 range=10:250:5
|
|
# @param top_n int 4 range=1:10:1
|
|
# @param max_weight float 0.25 range=0.05:1:0.05
|
|
|
|
def initialize(context):
|
|
g.universe = [
|
|
"USStock:AAPL", "USStock:MSFT", "USStock:NVDA", "USStock:AMZN", "USStock:META",
|
|
"USStock:GOOGL", "USStock:AVGO", "USStock:COST", "USStock:JPM", "USStock:XOM",
|
|
]
|
|
context.set_universe(g.universe)
|
|
context.set_benchmark("USStock:SPY")
|
|
context.subscribe(frequency="1d")
|
|
context.set_warmup(260)
|
|
run_weekly(rebalance, weekday=1, time="09:35")
|
|
|
|
|
|
def rebalance(context, data):
|
|
lookback = int(context.params.get("lookback", 60))
|
|
top_n = int(context.params.get("top_n", 4))
|
|
max_weight = float(context.params.get("max_weight", 0.25))
|
|
scores = {}
|
|
for symbol in g.universe:
|
|
bars = get_history(lookback + 1, "1d", "close", symbol)
|
|
if len(bars) < lookback + 1:
|
|
continue
|
|
returns = bars["close"].pct_change().dropna()
|
|
if len(returns):
|
|
scores[symbol] = float(returns.std())
|
|
selected = [symbol for symbol, _ in sorted(scores.items(), key=lambda item: item[1])[:top_n]]
|
|
for symbol in get_positions().keys():
|
|
if symbol not in selected:
|
|
order_target_percent(symbol, 0.0, reason="low_vol_removed")
|
|
weight = min(max_weight, 1.0 / len(selected)) if selected else 0.0
|
|
for symbol in selected:
|
|
order_target_percent(symbol, weight, reason="low_volatility")
|
|
$lowvol$, '{"params":[{"name":"lookback","type":"integer","default":60,"min":10,"max":250,"step":5,"labelKey":"strategyV2.params.lookback"},{"name":"top_n","type":"integer","default":4,"min":1,"max":10,"step":1,"labelKey":"strategyV2.params.topN"},{"name":"max_weight","type":"percent","default":0.25,"min":0.05,"max":1,"step":0.05,"labelKey":"strategyV2.params.maxWeight"}]}'::jsonb, '["strategy-v2","portfolio","cross-sectional","low-volatility","rotation"]'::jsonb, 'safety', 'cyan', 130, TRUE, '{"source":"system_seed","version":8,"apiVersion":2}'::jsonb, NOW()),
|
|
|
|
('strategy_v2_quality_growth', 'portfolio_strategy', 'Quality Growth Multi-Factor', 'A weekly point-in-time portfolio combining profitability, growth, and balance-sheet quality.', $quality$"""
|
|
Quality Growth Multi-Factor
|
|
Weekly point-in-time ranking by profitability, growth, and balance-sheet quality.
|
|
"""
|
|
|
|
# @param top_n int 5 range=1:10:1
|
|
# @param min_roe float 0.1 range=-1:1:0.01
|
|
# @param min_growth float 0 range=-1:5:0.01
|
|
# @param max_debt_to_equity float 2 range=0:10:0.1
|
|
# @param max_weight float 0.2 range=0.05:1:0.05
|
|
|
|
def initialize(context):
|
|
g.universe = [
|
|
"USStock:AAPL", "USStock:MSFT", "USStock:NVDA", "USStock:AMZN", "USStock:META",
|
|
"USStock:GOOGL", "USStock:AVGO", "USStock:COST", "USStock:JPM", "USStock:XOM",
|
|
]
|
|
context.set_universe(g.universe)
|
|
context.set_benchmark("USStock:SPY")
|
|
context.subscribe(frequency="1d")
|
|
context.set_warmup(10)
|
|
run_weekly(rebalance, weekday=1, time="09:35")
|
|
|
|
|
|
def rebalance(context, data):
|
|
top_n = int(context.params.get("top_n", 5))
|
|
min_roe = float(context.params.get("min_roe", 0.1))
|
|
min_growth = float(context.params.get("min_growth", 0.0))
|
|
max_debt = float(context.params.get("max_debt_to_equity", 2.0))
|
|
max_weight = float(context.params.get("max_weight", 0.2))
|
|
symbols = list(g.universe)
|
|
factors = get_fundamentals(["ROE", "REVENUE_GROWTH", "DEBT_TO_EQUITY"], symbols)
|
|
if factors.empty:
|
|
return
|
|
eligible = factors.dropna(subset=["ROE", "REVENUE_GROWTH", "DEBT_TO_EQUITY"])
|
|
eligible = eligible[(eligible["ROE"] >= min_roe) & (eligible["REVENUE_GROWTH"] >= min_growth) & (eligible["DEBT_TO_EQUITY"] <= max_debt)]
|
|
if eligible.empty:
|
|
selected = []
|
|
else:
|
|
score = eligible["ROE"].rank(pct=True) + eligible["REVENUE_GROWTH"].rank(pct=True) - eligible["DEBT_TO_EQUITY"].rank(pct=True)
|
|
selected = list(score.sort_values(ascending=False).head(top_n).index)
|
|
for symbol in get_positions().keys():
|
|
if symbol not in selected:
|
|
order_target_percent(symbol, 0.0, reason="quality_removed")
|
|
weight = min(max_weight, 1.0 / len(selected)) if selected else 0.0
|
|
for symbol in selected:
|
|
order_target_percent(symbol, weight, reason="quality_growth")
|
|
$quality$, '{"params":[{"name":"top_n","type":"integer","default":5,"min":1,"max":10,"step":1,"labelKey":"strategyV2.params.topN"},{"name":"min_roe","type":"number","default":0.1,"min":-1,"max":1,"step":0.01,"labelKey":"strategyV2.params.minRoe"},{"name":"min_growth","type":"number","default":0,"min":-1,"max":5,"step":0.01,"labelKey":"strategyV2.params.minGrowth"},{"name":"max_debt_to_equity","type":"number","default":2,"min":0,"max":10,"step":0.1,"labelKey":"strategyV2.params.maxDebtToEquity"},{"name":"max_weight","type":"percent","default":0.2,"min":0.05,"max":1,"step":0.05,"labelKey":"strategyV2.params.maxWeight"}]}'::jsonb, '["strategy-v2","portfolio","cross-sectional","fundamental","quality","growth"]'::jsonb, 'radar-chart', 'purple', 140, TRUE, '{"source":"system_seed","version":8,"apiVersion":2}'::jsonb, NOW())
|
|
ON CONFLICT (template_key) DO UPDATE SET
|
|
asset_type = EXCLUDED.asset_type,
|
|
title = EXCLUDED.title,
|
|
description = EXCLUDED.description,
|
|
code = EXCLUDED.code,
|
|
param_schema = EXCLUDED.param_schema,
|
|
tags = EXCLUDED.tags,
|
|
icon = EXCLUDED.icon,
|
|
accent = EXCLUDED.accent,
|
|
sort_order = EXCLUDED.sort_order,
|
|
is_active = TRUE,
|
|
metadata = EXCLUDED.metadata,
|
|
updated_at = NOW();
|