Does Price Rebound More Often After a Nine-Beat Count?

I want to use the Hoppy fictional teaching dataset in the current project to study this question: “After a completed falling Nine-Beat Count, is price more likely to rise than it is after an ordinary date?” Every company, identifier, and price in this dataset is fictional and exists only for learning. The result is not investment advice about any real security.

First inspect the current project structure. The teaching data will usually be in a folder named hoppy-teaching-dataset at the project root. It should contain README.md, data_dictionary.json, manifest.json, companies.parquet, stock_daily.parquet, and hs300_daily.parquet. Follow the actual files and fields you find. If a required Python analysis package is missing, install it with the project’s existing uv environment. Do not create a separate unrelated environment.

Use these fixed rules for the entire study:

1. The main study uses only completed daily falling Nine-Beat Counts. The program may also identify completed rising Nine-Beat Counts to verify direction changes, but rising Nine-Beat Counts must not enter the return study.
2. Sort each stock independently by trade_date, and count only along that stock’s own sequence of valid candles. Use the forward-adjusted closing price in adjusted_close for the comparison.
3. Let the current valid candle be t. C[t] > C[t-4] meets the up condition, C[t] < C[t-4] meets the down condition, and exact equality has no direction. Do not count when fewer than four earlier valid candles exist.
4. When the same directional condition continues, count upward from 1. If an unfinished sequence meets the opposite direction, end the old sequence immediately and make that same candle number 1 of the opposite direction. If the two closes are equal, end the unfinished sequence and return to waiting.
5. Reaching 9 completes one nine-beat count and locks that direction. Continued movement in the same direction must not display 10 or 11, and it must not immediately begin another sequence in the same direction. Equality unlocks the state and returns it to waiting. The opposite direction unlocks the state and makes that same candle number 1 of the new direction.
6. If a stock has no daily candle on a market trading date, do not create a placeholder candle and do not interrupt the sequence merely because of that gap. When data resumes, continue along the stock’s own valid-candle sequence.
7. Use the dates in hs300_daily.parquet as the course market calendar. The data screen may check whether company-date records exist from 2021-01-04 through 2023-12-29, but it must not read or use 2023 price direction, nine-beat signals, or returns. If a stock has any single continuous gap strictly longer than 15 market trading days, exclude the entire stock before signal detection. A 15-day gap remains eligible; a 16-day gap does not.
8. Detect nine-beat signals only in the rule-development period from 2021-01-04 through 2022-12-30. The period from 2023-01-03 through 2023-12-29 is the final holdout. Until the complete entry and exit rules have been frozen, do not calculate or reveal its price direction, nine-beat signals, or returns.
9. A signal is confirmed only after the close of the candle carrying number 9. Use the adjusted_open of the next valid candle as the observation entry. Count that entry candle as candle 1, then use the adjusted_close of valid candle 3, 5, 10, or 20 as the observation endpoint. Return equals “endpoint close ÷ entry open − 1.” An event without enough future data is excluded from that window, but it may remain eligible for a shorter complete window.
10. Random comparison dates must come from the same stock and the same development period. Draw only from valid dates with a complete future window, and exclude actual completed falling Nine-Beat Count dates. Treat a random date as an ordinary “signal completion date”: use the next valid candle’s open as the entry and the same endpoint rule for the return. For each stock and window, draw as many random dates per round as that stock has eligible nine-beat events. Do not draw the same date twice for one stock in the same round. Run 1,000 independent rounds for each window, and record the random-number tool and seed so the same implementation can be rerun. The course reference uses NumPy default_rng, a base seed of 20260903, and “base seed + window length” for each window. If you use another reproducible implementation, do not force the output to match the course decimals.
11. Ten valid candles is the primary observation window chosen in advance. The first weak-pattern threshold is: the actual 10-candle mean return must exceed both the 95th percentile and the median of the random experiments’ mean returns. Whether it passes or fails, do not describe the nine-beat count as “proven effective” or turn it into real-world investment advice.

Split the work into two stages. Complete only stage one now, then stop for my confirmation:

• inspect the data files and fields, then complete the data screen;
• use simple, manually constructed price sequences to test t-4, equality interruption, direction changes, locking after 9, and continuation across a missing candle;
• identify completed rising and falling Nine-Beat Counts in the development period;
• report the starting company count, exclusions and reasons, final company count, and totals for completed rising and falling Nine-Beat Counts;
• sample several representative signals from the fictional teaching data and draw charts labelled from 1 through 9, covering an ordinary completion, post-completion locking, a count crossing a data gap, and a direction change where possible;
• save a plain-language validation summary and PNG sample charts, then tell me where they are.

During stage one, do not calculate event returns or run the random-date comparison. Do not use the CSI 300 as a return benchmark. Do not add MACD, volume, market capitalization, industry, position sizing, fees, or slippage. Do not read or reveal 2023 prices, signals, or returns.

You may choose the program structure and filenames, and you may run any necessary internal checks. Do not treat program code as the main deliverable. When finished, explain in plain language what you checked, what you found, and what remains undone. Then stop and wait for me to inspect the chart samples.

These chart samples show no obvious problem. Freeze the eligible-company list and nine-beat signals you just produced, and do not change the confirmed rules. Now calculate event returns over 3, 5, 10, and 20 valid candles. For each window, run 1,000 random-date comparisons using the same-stock method from the original task.

Report the eligible event count, mean return, median return, and positive-return share for each window. Also report the median and 5th–95th percentile range of the random experiments’ mean returns, and show where the actual result sits within the random results. Produce one readable comparison chart and save a plain-language research summary.

Do not read or reveal 2023 prices, signals, or returns. Do not add the CSI 300, MACD, volume, market capitalization, industry, position sizing, fees, or slippage. When finished, tell me where the outputs are and explain what the result currently supports and what it cannot support.

I want to audit whether the nine-beat experiment I just completed followed the agreed rules. Read the data files, program, and summary actually used in the current project. Do not guess any external reference answer, and do not modify the rules merely to make the result look more plausible. Keep 2023 prices, nine-beat signals, and returns sealed.

Audit the calculation chain from the raw data onward. In order, report: the data package, fields, and date range actually read; company and date counts; the exclusion process for any continuous gap strictly longer than 15 market trading days; the forward-adjusted field; the t-4 comparison, equality interruption, direction change, and post-completion lock; the entry after signal confirmation; the counting of the 3-, 5-, 10-, and 20-candle windows; and the actual company, signal, event, and nine-beat return totals left at each stage.

Then audit the random-date method separately. Confirm same stock, matched count, complete future window, exclusion of actual nine-beat completion dates, and no duplicate date within the same stock and round. Record the random-number tool, candidate-date ordering, and seed actually used. Run the same program twice and compare the results.

Finally, produce an audit table with at least these columns: stage, rule used, actual result, anomaly found, and possible impact. If you find a problem, identify the earliest stage where it appears and explain the repair in plain language. If you do not find a problem, state whether small random-result differences would change the current judgment. Do not modify the program or research outputs until I confirm.