中文
COMBO-04 · BTC-USD

Trend + Mean Reversion Hybrid · Strategy evidence

Inspect the inputs, parameters and accounting behind a published result.

Downloads Back to strategy
Run reference811b3deb4e5f
Last included price bar2026-11-10
Bundle generated (UTC)2026-11-11 10:55 UTC
Accounting modelSynthetic daily target allocations · v3
Replay the recorded returns and equity with the files below. Recreating strategy decisions needs the original matching inputs; fresh price downloads may differ. These are simulated allocations, not broker fills.

Downloads

Downloads load and verify compressed objects only when selected. Negative weights represent short exposure; no code is executed in your browser.

1. Download a complete bundle

Download the Python tool above and run this command. It checks shared objects and creates replay.zip; no repository clone is needed.

python e0543584b55263609cc8fd38cd032cb8432af6c2b48da3f53599e6e68c922f59.py https://quantarenas.com/data/evidence/runs/811b3deb4e5fa8801fb2edfff413c6990deb3f451bbcb526b8183fd89f559d11/manifest.json --bundle replay.zip

2. Replay the arithmetic

Extract replay.zip and run from its folder with Python. This verifies recorded weights, contributions and equity using only the standard library; it does not recalculate strategy decisions.

python scripts/replay_evidence.py data/evidence/runs/811b3deb4e5fa8801fb2edfff413c6990deb3f451bbcb526b8183fd89f559d11/manifest.json

3. Obtain and compare inputs

In the extracted bundle, use the recorded Python version and install the pinned dependencies. This command downloads provider inputs privately and reports inputHashMatches. Only a matching hash supports exact recomputation; revised provider data may not match. You may instead supply an original matching snapshot as private-input.json.

python -m pip install -r requirements-lock.txt
python scripts/recompute_evidence.py data/evidence/runs/811b3deb4e5fa8801fb2edfff413c6990deb3f451bbcb526b8183fd89f559d11/manifest.json --fetch-input private-input.json

4. Recompute the original strategy

With the exact original input snapshot and recorded dependencies, run the archived strategy and walk-forward selection. Original provider inputs are not public. Revised downloads fail the input-hash match and cannot establish exact reproduction.

python scripts/recompute_evidence.py data/evidence/runs/811b3deb4e5fa8801fb2edfff413c6990deb3f451bbcb526b8183fd89f559d11/manifest.json --input private-input.json
Accounting assumptions

Each day's return uses the preceding day's target allocation. The new target is decided at the current close. This is a synthetic close-based model, not a measured execution price.

Cash residual
1 - sum(weights); residual may exceed one or be negative for shorts/leverage.
Decision
Target weights use bars through close t; previous targets earn (t-1,t].
Trading costs (bp)
0
Execution assumption
Synthetic daily target allocations, not broker orders or fills; financing/borrow costs omitted.
Synthetic rebalance turnover
Sum(abs(target[t] - target[t-1]*(1+assetReturn[t])/(1+grossReturn[t]))); one-way risky notional at close t after returns, divided by pre-trade equity.
Return interval
Sum of previous target weight times each asset close-to-close return; cash return is zero.
Target weight change
Sum(abs(target[t] - target[t-1])); changes in rule targets only.
Walk-forward transition
Candidate retains its own hypothetical state; first test return uses previous published target.

Parameters used

{
  "active": {
    "max_hold": 20,
    "rsi_exit": 50,
    "rsi_oversold": 35,
    "rsi_period": 14,
    "trend_ma": 160
  },
  "defaults": {
    "max_hold": 20,
    "rsi_exit": 50,
    "rsi_oversold": 35,
    "rsi_period": 14,
    "trend_ma": 200
  }
}

Walk-forward selections

Default/pre-walk-forward history is not out-of-sample evidence. Each fold records its training and test boundaries; the selected candidate retains its own hypothetical state.

1 · 2017-09-17T00:00:00 → 2018-03-17T00:00:00
{
  "config": {
    "max_hold": 20,
    "rsi_exit": 50,
    "rsi_oversold": 35,
    "rsi_period": 17,
    "trend_ma": 160
  },
  "isSharpe": 0.5219328058059743,
  "run": 6,
  "testEnd": "2018-03-17T00:00:00",
  "testStart": "2017-09-17T00:00:00",
  "trainEnd": "2017-09-16T00:00:00",
  "trainStart": "2014-09-17T00:00:00"
}
2 · 2018-03-18T00:00:00 → 2018-09-18T00:00:00
{
  "config": {
    "max_hold": 20,
    "rsi_exit": 50,
    "rsi_oversold": 35,
    "rsi_period": 17,
    "trend_ma": 160
  },
  "isSharpe": 0.4332055123191974,
  "run": 6,
  "testEnd": "2018-09-18T00:00:00",
  "testStart": "2018-03-18T00:00:00",
  "trainEnd": "2018-03-17T00:00:00",
  "trainStart": "2015-03-18T00:00:00"
}
3 · 2018-09-19T00:00:00 → 2019-03-19T00:00:00
{
  "config": {
    "max_hold": 20,
    "rsi_exit": 50,
    "rsi_oversold": 35,
    "rsi_period": 17,
    "trend_ma": 160
  },
  "isSharpe": 0.6494960019803916,
  "run": 6,
  "testEnd": "2019-03-19T00:00:00",
  "testStart": "2018-09-19T00:00:00",
  "trainEnd": "2018-09-18T00:00:00",
  "trainStart": "2015-09-19T00:00:00"
}
4 · 2019-03-20T00:00:00 → 2019-09-20T00:00:00
{
  "config": {
    "max_hold": 20,
    "rsi_exit": 50,
    "rsi_oversold": 35,
    "rsi_period": 17,
    "trend_ma": 160
  },
  "isSharpe": 0.4739778146320534,
  "run": 6,
  "testEnd": "2019-09-20T00:00:00",
  "testStart": "2019-03-20T00:00:00",
  "trainEnd": "2019-03-19T00:00:00",
  "trainStart": "2016-03-20T00:00:00"
}
5 · 2019-09-21T00:00:00 → 2020-03-21T00:00:00
{
  "config": {
    "max_hold": 20,
    "rsi_exit": 50,
    "rsi_oversold": 35,
    "rsi_period": 17,
    "trend_ma": 160
  },
  "isSharpe": 0.6330118788211069,
  "run": 6,
  "testEnd": "2020-03-21T00:00:00",
  "testStart": "2019-09-21T00:00:00",
  "trainEnd": "2019-09-20T00:00:00",
  "trainStart": "2016-09-21T00:00:00"
}
6 · 2020-03-22T00:00:00 → 2020-09-22T00:00:00
{
  "config": {
    "max_hold": 20,
    "rsi_exit": 50,
    "rsi_oversold": 35,
    "rsi_period": 17,
    "trend_ma": 160
  },
  "isSharpe": 0.2510712527361324,
  "run": 6,
  "testEnd": "2020-09-22T00:00:00",
  "testStart": "2020-03-22T00:00:00",
  "trainEnd": "2020-03-21T00:00:00",
  "trainStart": "2017-03-22T00:00:00"
}
7 · 2020-09-23T00:00:00 → 2021-03-23T00:00:00
{
  "config": {
    "max_hold": 20,
    "rsi_exit": 50,
    "rsi_oversold": 35,
    "rsi_period": 11,
    "trend_ma": 160
  },
  "isSharpe": 0.2429398175433583,
  "run": 0,
  "testEnd": "2021-03-23T00:00:00",
  "testStart": "2020-09-23T00:00:00",
  "trainEnd": "2020-09-22T00:00:00",
  "trainStart": "2017-09-23T00:00:00"
}
8 · 2021-03-24T00:00:00 → 2021-09-24T00:00:00
{
  "config": {
    "max_hold": 20,
    "rsi_exit": 50,
    "rsi_oversold": 35,
    "rsi_period": 11,
    "trend_ma": 160
  },
  "isSharpe": 0.6500628032320712,
  "run": 0,
  "testEnd": "2021-09-24T00:00:00",
  "testStart": "2021-03-24T00:00:00",
  "trainEnd": "2021-03-23T00:00:00",
  "trainStart": "2018-03-24T00:00:00"
}
9 · 2021-09-25T00:00:00 → 2022-03-25T00:00:00
{
  "config": {
    "max_hold": 20,
    "rsi_exit": 50,
    "rsi_oversold": 35,
    "rsi_period": 11,
    "trend_ma": 160
  },
  "isSharpe": 0.7443888999158027,
  "run": 0,
  "testEnd": "2022-03-25T00:00:00",
  "testStart": "2021-09-25T00:00:00",
  "trainEnd": "2021-09-24T00:00:00",
  "trainStart": "2018-09-25T00:00:00"
}
10 · 2022-03-26T00:00:00 → 2022-09-26T00:00:00
{
  "config": {
    "max_hold": 20,
    "rsi_exit": 50,
    "rsi_oversold": 35,
    "rsi_period": 11,
    "trend_ma": 160
  },
  "isSharpe": 0.5841557975921395,
  "run": 0,
  "testEnd": "2022-09-26T00:00:00",
  "testStart": "2022-03-26T00:00:00",
  "trainEnd": "2022-03-25T00:00:00",
  "trainStart": "2019-03-26T00:00:00"
}
11 · 2022-09-27T00:00:00 → 2023-03-27T00:00:00
{
  "config": {
    "max_hold": 20,
    "rsi_exit": 50,
    "rsi_oversold": 35,
    "rsi_period": 11,
    "trend_ma": 160
  },
  "isSharpe": 0.57972638566925,
  "run": 0,
  "testEnd": "2023-03-27T00:00:00",
  "testStart": "2022-09-27T00:00:00",
  "trainEnd": "2022-09-26T00:00:00",
  "trainStart": "2019-09-27T00:00:00"
}
12 · 2023-03-28T00:00:00 → 2023-09-28T00:00:00
{
  "config": {
    "max_hold": 20,
    "rsi_exit": 50,
    "rsi_oversold": 35,
    "rsi_period": 11,
    "trend_ma": 160
  },
  "isSharpe": 0.9257495539462678,
  "run": 0,
  "testEnd": "2023-09-28T00:00:00",
  "testStart": "2023-03-28T00:00:00",
  "trainEnd": "2023-03-27T00:00:00",
  "trainStart": "2020-03-28T00:00:00"
}
13 · 2023-09-29T00:00:00 → 2024-03-29T00:00:00
{
  "config": {
    "max_hold": 20,
    "rsi_exit": 50,
    "rsi_oversold": 35,
    "rsi_period": 11,
    "trend_ma": 160
  },
  "isSharpe": 0.6404790394457704,
  "run": 0,
  "testEnd": "2024-03-29T00:00:00",
  "testStart": "2023-09-29T00:00:00",
  "trainEnd": "2023-09-28T00:00:00",
  "trainStart": "2020-09-29T00:00:00"
}
14 · 2024-03-30T00:00:00 → 2024-09-30T00:00:00
{
  "config": {
    "max_hold": 20,
    "rsi_exit": 50,
    "rsi_oversold": 35,
    "rsi_period": 11,
    "trend_ma": 160
  },
  "isSharpe": 0.33180598069703676,
  "run": 0,
  "testEnd": "2024-09-30T00:00:00",
  "testStart": "2024-03-30T00:00:00",
  "trainEnd": "2024-03-29T00:00:00",
  "trainStart": "2021-03-30T00:00:00"
}
15 · 2024-10-01T00:00:00 → 2025-04-01T00:00:00
{
  "config": {
    "max_hold": 20,
    "rsi_exit": 50,
    "rsi_oversold": 35,
    "rsi_period": 11,
    "trend_ma": 200
  },
  "isSharpe": 0.38669340333636776,
  "run": 1,
  "testEnd": "2025-04-01T00:00:00",
  "testStart": "2024-10-01T00:00:00",
  "trainEnd": "2024-09-30T00:00:00",
  "trainStart": "2021-10-01T00:00:00"
}
16 · 2025-04-02T00:00:00 → 2025-10-02T00:00:00
{
  "config": {
    "max_hold": 20,
    "rsi_exit": 50,
    "rsi_oversold": 35,
    "rsi_period": 14,
    "trend_ma": 160
  },
  "isSharpe": 0.27817157107256074,
  "run": 3,
  "testEnd": "2025-10-02T00:00:00",
  "testStart": "2025-04-02T00:00:00",
  "trainEnd": "2025-04-01T00:00:00",
  "trainStart": "2022-04-02T00:00:00"
}
17 · 2025-10-03T00:00:00 → 2026-04-03T00:00:00
{
  "config": {
    "max_hold": 20,
    "rsi_exit": 50,
    "rsi_oversold": 35,
    "rsi_period": 14,
    "trend_ma": 160
  },
  "isSharpe": 0.6947064696815586,
  "run": 3,
  "testEnd": "2026-04-03T00:00:00",
  "testStart": "2025-10-03T00:00:00",
  "trainEnd": "2025-10-02T00:00:00",
  "trainStart": "2022-10-03T00:00:00"
}
18 · 2026-04-04T00:00:00 → 2026-10-04T00:00:00
{
  "config": {
    "max_hold": 20,
    "rsi_exit": 50,
    "rsi_oversold": 35,
    "rsi_period": 14,
    "trend_ma": 160
  },
  "isSharpe": 0.4069931050540292,
  "run": 3,
  "testEnd": "2026-10-04T00:00:00",
  "testStart": "2026-04-04T00:00:00",
  "trainEnd": "2026-04-03T00:00:00",
  "trainStart": "2023-04-04T00:00:00"
}
19 · 2026-10-05T00:00:00 → 2026-11-10T00:00:00
{
  "config": {
    "max_hold": 20,
    "rsi_exit": 50,
    "rsi_oversold": 35,
    "rsi_period": 14,
    "trend_ma": 160
  },
  "isSharpe": 0.5728666585310037,
  "run": 3,
  "testEnd": "2026-11-10T00:00:00",
  "testStart": "2026-10-05T00:00:00",
  "trainEnd": "2026-10-04T00:00:00",
  "trainStart": "2023-10-05T00:00:00"
}

Run summary

Price observations4438
Ending equity1.7904
Total return79.04%
Days with target changes107
Cumulative target change10700.00%
Cumulative synthetic rebalance turnover10700.00%
Target gross exposure100.00%
Target net exposure100.00%
Target cash residual0.00%
Effective gross exposure100.00%
Effective net exposure100.00%
Effective cash residual0.00%

Latest asset weights

MarketTargetEffective
BTC-USD100.00%100.00%

Target change measures changes in rule weights. Synthetic rebalance turnover also accounts for weight drift after returns; even unchanged targets can require rebalancing. Cash is the signed residual after net exposure.

Accounting changes on identical inputs

{
  "changedReturnDays": 0,
  "correctedTerminalEquity": 1.7903976059159044,
  "legacyTerminalEquity": 1.7903976059159044,
  "maximumDailyReturnDelta": 0.0,
  "model": "coherent-lagged-target-v2",
  "terminalEquityDelta": 0.0
}
Full run identity and technical record
{
  "inputHash": "5b3c8b854be76fa4b63c345c5aed9398c52122fb629845e8ed9de94de74eecd0",
  "ruleVersion": null,
  "sameInputAccountingCorrection": {
    "changedReturnDays": 0,
    "correctedTerminalEquity": 1.7903976059159044,
    "legacyTerminalEquity": 1.7903976059159044,
    "maximumDailyReturnDelta": 0.0,
    "model": "coherent-lagged-target-v2",
    "terminalEquityDelta": 0.0
  },
  "sourceHashes": {
    "scripts/build_data.py": "e3dcc988d80a46f7f5a24396bb4ffcac240da8e1249de18a5a282eacccc52320",
    "scripts/enrich_metrics.py": "05611fa9c6de11c261f631cdd8a3314212e7c567f24878a396d057d88752051c",
    "scripts/evidence_v2.py": "c4c6523dbbb9513464dca25150f42cbaac7cbfd71bd8afdc855659b2c763d72e",
    "scripts/performance_io.py": "42282ff10697ceb56dc7ca2edf79538c2e1e8344da8e6d69ceac4d16ba5c668a",
    "scripts/recompute_evidence.py": "a7f59004cb7c356940ee3748439024cd83cac434acb90c0bead2ae19f81be127",
    "scripts/replay_evidence.py": "4e04d937c19b9040da6dc9c3c80ac22181b45ffba0131f3a63a4def95865fad6",
    "scripts/requirements.txt": "7424048072a2cf3d8c3b4e4f37a5b36c1e7ff3732d893c43295d17d85c5f97d1",
    "scripts/strategies/trend_mr_hybrid.py": "b9a932ee3be636120cdd58f8f2795bdeda323a48c2395f676a9a9e719de3a9c2",
    "scripts/strategy_evidence.py": "a3a93d62a84298470af3eb8e7d74e9f0ca0d29cf672d1d2d5449b41904e3d58c"
  },
  "runId": "811b3deb4e5fa8801fb2edfff413c6990deb3f451bbcb526b8183fd89f559d11",
  "generatedAt": "2026-11-11T10:55:31.449953+00:00",
  "executionModel": {
    "cash": "1 - sum(weights); residual may exceed one or be negative for shorts/leverage.",
    "decision": "Target weights use bars through close t; previous targets earn (t-1,t].",
    "feesBasisPoints": 0,
    "fills": "Synthetic daily target allocations, not broker orders or fills; financing/borrow costs omitted.",
    "id": "synthetic-daily-target-v3",
    "rebalanceTurnover": "Sum(abs(target[t] - target[t-1]*(1+assetReturn[t])/(1+grossReturn[t]))); one-way risky notional at close t after returns, divided by pre-trade equity.",
    "return": "Sum of previous target weight times each asset close-to-close return; cash return is zero.",
    "targetChange": "Sum(abs(target[t] - target[t-1])); changes in rule targets only.",
    "wfo": "Candidate retains its own hypothetical state; first test return uses previous published target."
  }
}

Get the weekly Arena report

One email a week: ranking changes, new signals and new research. No spam; unsubscribe any time.