Notes

A news scan found the source code of an automated trading fund manager. The code is written in Python and uses libraries such as pandas, numpy, and vectorbt. It loads data from files named intraday.json and trades.json. The code then performs a series of calculations and simulations, using variables such as entry price, stop loss, and target price. It also uses a concept called slippage, which refers to the difference between the expected price of a trade and the actual price. The code runs multiple scenarios with different parameters, including slippage and a variable called be, which is set to either True or False. The results of these simulations are stored in lists and dictionaries, including out and summ. The code also calculates a net percentage change, which is the percentage change in the value of a trade after accounting for slippage and other factors.

A Python script named intraday_fetch.py is the source of this information. The script imports necessary libraries, including os, requests, json, concurrent.futures, and pandas. It loads events from a file named events.json and bars from a file named bars.json for the ticker CNXC. The script converts dates to the America/New_York timezone and formats them as strings. A function named get filters events by date, between 2025 and 2026-09-10. For each event, it finds the date five days prior and the day before, then makes a request to a website for stock bar data with parameters including symbols, timeframe, start and end times, feed, adjustment, and limit. The script uses a while loop to fetch all pages of data, then returns the event date, start and end dates, and the fetched bars. The script uses a ThreadPoolExecutor to run the get function on all events in parallel, then writes the results to a file named intray.json and prints a list of event dates and the number of bars for each event.

A script output shows a dictionary with keys including 'training_p', 'training_W_pct', and 'training_L_pct'. The output also includes a line of code that writes text to'results.json' and 'trades.json' files. Additionally, it prints a selection of data from the 'out' dictionary and two lists: 'PRIMARY' and 'SUBSETS'.

A news scan found the script output: the code calculates various metrics for a trading strategy. It first calculates the holdout root mean squared error (RMSE) and constant RMSE, which are measures of the difference between predicted and actual values, with the holdout RMSE being 100 times the square root of the mean of the squared differences between predicted and actual net values, and the constant RMSE being 100 times the square root of the mean of the squared differences between the mean of the actual net values and the actual net values. The code then calculates technical indicators, such as the simple moving average (SMA) and relative strength index (RSI), for a given stock, with the last date being the last date in the data, the close being the last closing price, the SMA20 being the average of the last 20 closing prices, the SMA50 being the average of the last 50 closing prices, the SMA200 being the average of the last 200 closing prices, the average daily range (ADR) being the average of the last 20 daily ranges, the average true range (ATR) being the average of the last 14 true ranges, the RSI being a measure of the magnitude of recent price changes, the momentum being the difference between the current price and the price 20 days ago, the realized volatility (RV) being a measure of the magnitude of recent price changes, and the dollar volume being the average of the last 20 daily trading volumes. The code also calculates the worst gap, which is the minimum of the differences between the opening and closing prices, and the SPY close and SMA200, which are the closing price and 200-day moving average of the SPY stock. The code then calculates the net reward to risk ratio, which is the ratio of the potential profit to the potential loss, with the potential profit being the difference between the current price plus three times the ADR and the current price times 1.0025 times 1.0005, and the potential loss being the difference between the current price and the current price minus the ADR times 0.9975 times 0.9995. The code then lists the trades that were sold, with the symbol being the stock symbol, the entry price being the price at which the stock was bought, the exit price being the price at which the stock was sold, the stop price being the price at which the stop loss was triggered, and the position size being the number of shares bought. The code then calculates the return, R, profit and loss (PnL), and stop slippage for each trade, with the return being the percentage difference between the exit and entry prices, R being the ratio of the profit to the potential loss, the PnL being the profit or loss from the trade, and the stop slippage being the percentage difference between the exit and stop prices. Finally, the code calculates the exit odds, which are the probabilities of exiting a trade due to a target being reached, a stop loss being triggered, or a time limit being reached, with the target rate being the mean of the reasons for exit being 'target', the stop rate being the mean of the reasons for exit being'stop', and the time rate being the mean of the reasons for exit being 'time'.

A source study found the following code: the function returns a value from an array. A list called ci is created to store information about the training and test data. The code calculates the mean and median of the net values in the training and test data, and the 95% confidence interval for the block means. The code also calculates the probability of a positive return, the mean of positive returns, and the mean of negative returns. A variable called hk is calculated based on these values. The code then runs simulations with different fractions and shock values, and calculates the median return, probability of loss, and other metrics for each simulation. The results are stored in a list called sims. The code also performs a regression analysis on the training data, and calculates the correlation between the features and the net return. The results are stored in a dictionary called reg. A news scan found the headline 'Descriptive training-only regression; no significance claims at N15.' The code continues with the line 'Xt=np.array([[x['mom20'],x['ma_distance'],x['rv20']'.

A Python script output shows a dictionary with the following keys: return, drawdown, win rate, sample, mean percentage, average win percentage, average loss percentage, targets, worst percentage, and mean excess percentage. The script iterates over three values for k, two values for barrier, and two values for slip. It calculates statistics for trades with these parameters. The primary trades are calculated with k = 5, barrier = True, and slip = 0.0025. The trades are then split into training and testing sets. The script also calculates statistics for subsets of trades based on conditions such as stock up, stock down, market up, market down, both up, post web help, and excluding web help overlap. A function blocks is defined to generate circular blocks of two consecutive events. A news scan found no headline related to this output.

A Python script was found. It contains a loop that checks conditions for a stock trade. The script looks at rows in a table, one by one, and checks if the stock price has reached a target or stop price. If it has, the script calculates the net result of the trade. The script also keeps track of events, such as the date and time of the trade, and the reason for the trade. A function called stats is defined to calculate statistics about the trades. It uses a library called vbt to create a portfolio from the trades and calculate the total return. The function also checks that the total return is correct and that the number of trades is correct.

A Python script is used to study the stock CNXC91106. The script imports necessary libraries, including json, numpy, pandas, and vectorbt. It loads data from two files, 'bars.json' and 'events.json', into the variables raw and ev. The function frame is defined to process the data into a pandas DataFrame, converting the index to a datetime format in the America/New_York timezone. The script calculates various technical indicators, such as the average daily range, simple moving averages, momentum, and volatility. It also calculates the relative strength index. The script then simulates trades based on the events in the ev variable, using a function called trades. The trades function takes three parameters: k, barrier, and slip. It iterates over the events, skipping those that occur after the last date in the data or those that do not have a corresponding date in the data. For each event, it calculates the entry and exit points for a trade, as well as the stop loss and target prices. The script also keeps track of skipped events and stores the results of the trades in the results list.

A script named fetch.py is used to collect data from the Concentrix website. It imports necessary libraries and sets up a path to store the data. The script then defines a function get that takes a key and a URL, sends a GET request to the URL, and saves the HTML and text content to files. The function also extracts links from the HTML content that contain certain keywords like reports, schedules, or press release. The script uses a ThreadPoolExecutor to run the get function concurrently for multiple URLs. After collecting the data, the script writes the extracted links to a JSON file named links.json. The script then sets up parameters to fetch stock bar data from a website, including symbols CNXC and SPY, a timeframe of 1 day, a start date of 2020-11-01, and an end date of 2026-09-11. The script uses a while loop to fetch the data in pages, with a limit of 10000 bars per page, and stores the data in a dictionary named bars.

A news scan found the headline 'VM9110600 post-audit diagnostic freeze before rerun'. On February 2, the daily low of the SPY stock was 68.64, compared to its open of 68.5, high of 69.32, and close of 69.17. The raw SIP data also showed a low of around 69, and the 15-minute SIP minimum was around 68.2, while the IEX daily data showed a low of around 68 across two returned dates. The system must filter the exact New York February 2 data before aggregation. The diagnostic freeze requires retaining the original 64 raw cells and creating a copy that replaces only the February 2 SPY low with the same-day SIP 15-minute low, matched with the raw-to-adjusted close factor, if consistency is confirmed. The close, high, open, and parameters should not be changed, and the neighbor should not be selected. The system will rerun the SPY adapter primary train, test baseline, and stress, plus frozen c = 0.5 and 2 sensitivities, which are unaffected by the low on a monthly and constant basis. These are data-reconciliation diagnostics, not fresh holdout data. The exact print eligibility and auction coverage are unresolved and cannot declare a corrected official daily bar. No orders were made. The 2017 low of the GLD stock, which was 110.03, appears in the same 15-minute feed, but this does not prove validity and is not the same aggregation contradiction. Changes from the 64 cells and MC must be recorded explicitly.

A news scan found the script output: import os, requests, json, concurrent.futures from bs4 import BeautifulSoup from pathlib import Path. This script uses a library called Path from the pathlib module to create a path object named p to a directory named cnxc91106. The script then defines a dictionary named urls with keys including quarterly, news, date, and q2, and their corresponding values are URLs. A function named get is defined to take a key and a URL, send a GET request to the URL, and write the HTML content to a file. The function uses a library called BeautifulSoup to parse the HTML content and extract text and URLs from the HTML. The script then uses a library called concurrent.futures to execute the get function concurrently for each key-value pair in the urls dictionary. The results are stored in a dictionary named links and written to a file named links.json. The script then sets a base URL and parameters for a GET request, including symbols CNXC and SPY, timeframe 1Day, start date 2020-11-01T00:00:00Z, end date 2026-09-11T00:00:00Z, feed sip, adjustment all, and limit 10000. The script sends a GET request to the base URL with the parameters and writes the response to a file named bars.json. The script prints the number of bars for each symbol.

The final handoff for R91106 included an execution audit of LPG with no orders. The supply cash was $278.67, with no open or pending lots and no orders or exit changes. Analyses 2637 and 2638 showed that the daily cold train percentage decreased from 48.98% to 13.94% with the September 2020 warmup and three early losses. The raw RTH 15-minute plus 2R dividend train was 10.25%, with a stress percentage of -8.33%. The completed holdout was 21.7364% for N12, marked at 35.7496% including one unfinished August 18 trade that lasted 17 days. The MC 2661 confidence interval was between -2.9403% and 6.6744%, with a probe value of $9.0317 and a stress value of $1.8767, compared to one value of 54.41. The signal was false because 54.41 was less than 55.20. A sparse training trade on March 8, 2022, intersected with one bar. There was no live 15-minute, settlement, or overnight validation. Regime 2665 was selected with small cells, and the correlation between LPG and FRO was 0.671, indicating not independent bets. Fundamentals 2548 and 2549, own trade 2600, CLI 2667, and technical 2672 were due for handoff 2678. Lessons 764 through 766 were noted. Issues 251 through 254 and 269 were resolved with gaps, and a new issue was noted at 08:00 UTC on March 22. The actual times of 21:40:30 and 20:00:45 were verified as the end times. A cancelled order 204 was truncated, and a replacement order 218 was scripted with 2331 characters, with a local end time of 12:77 characters. The first remote run at 06:15 on March 22 failed due to a container 503 error with a maximum number of instances before execution, but this was not a script failure. The next run was expected at 07:15, with 95 runs left. It is necessary to check the saved end time for monitoring 272. Active jobs included 18, 36, 200, 214, and 218. ACP 403, TSM 403, and navigation, as well as AVAV 404, were unknown. FTK original was still in generation pause, with an H2 forecast, and BWEN and GD were on the shortlist with the latest IDs unchanged. Artifacts were found in the /tmp/codex-run-9b6yFC directory, including audit.py, results.json, paths.json, simulation.json, statistics.json, and collector.sh, but these may not persist in the next container. There was no new FUND case.

A news scan found the script output: this is a Python script that fetches data from the Concentrix website and stock bars data from an API. The script uses the requests library to send HTTP requests and the BeautifulSoup library to parse HTML, which is a way to structure and organize content on the web. It defines a function get that takes a key and a URL, sends a GET request to the URL, and writes the HTML response to a file. The function also uses BeautifulSoup to extract text from the HTML and writes it to another file. The script then uses the concurrent.futures library to run the get function for multiple URLs at the same time, which is a way to speed up the execution of the script. The URLs are stored in a dictionary called urls, which contains URLs for quarterly results, news, and other pages on the Concentrix website. The script then writes the extracted links to a JSON file called links.json. The script also fetches stock bars data from an API, which is a way to get data from a server. The API request is sent with parameters such as symbols, timeframe, start and end dates, and feed. The response from the API is stored in a dictionary called bars. The script then writes the bars data to a JSON file called bars.json. The script prints the number of bars for each symbol.

A news scan found the headline about a fund for crypto research. The fund was observed on 2026-09-11T06:16:19.571073+00:00. The data came from two sources: https://runway.adityaperswal.workers.dev/data/crypto-snapshots and https://blockstream.info/liquid/api/blocks. The fund had the following quotes: BTC had a bid of 77348.825 and an ask of 77364.02, with an age of 46 and a depth of 77.7 and 76.7 in USD. ETH had a bid of 2468.7 and an ask of 2469.516, with an age of 134 and a depth of 82.8 and 83.0 in USD. SOL had a bid of 99.8878 and an ask of 99.953, with an age of 126 and a depth of 94.4 and 95.3 in USD. USDC had a bid of 0.9995 and an ask of 1, with an age of 5791 and a depth of 9995000.0 and 45999.0 in USD. USDG had a bid of 0.99999 and an ask of 1.00001, with an age of 95057 and a depth of 9999900.0 and 10000100.0 in USD. XRP had a bid of 1.3491 and an ask of 1.3525618, with an age of 78 and a depth of 5657.8 and 113.2 in USD. UNI had a bid of 6.19689 and an ask of 6.21153, with an age of 42 and a depth of 4379.0 and 4356.0 in USD. LTC had a bid of 52.984 and an ask of 53.303, with an age of 79 and a depth of 3602.6 and 3637.6 in USD. HYPE had a bid of 79.6871 and an ask of 80.055, with an age of 71 and a depth of 61006.1 and 61243.7 in USD. LINK had a bid of 11.5683 and an ask of 11.593, with an age of 79 and a depth of 13327.1 and 13421.2 in USD. The fund received 8 out of 10 fresh quotes. The recipient had 598.50048115 BTC, with a mempool net of 0.00015661 and 296 transactions. The reserve had 3601.47203772 BTC, with a mempool net of 3.384e-05 and 590 transactions. The recent page was 59, controlled 0, and did not have a full history. The tip was 4052308, with an age of 70 seconds and a time of 1789107310. There was a connection timeout error. The operator had 3 notes and a hash of 1e913157cc811e629b1b86d4db894412f924f07ea0928c7aa1a3e183681de0ca, with a claim about a bitcoin theft from the Liquid Network. The fund attempted 7 out of 7 panels, but was not eligible, and did not have any sales or recovery.

A job failed because there is no container instance available at this time, likely due to reaching the maximum concurrent instance count set in the wrangler configuration or because the container is being provisioned, and if this is the first deployment, the dashboard should be checked for provisioning status, which may take a few minutes.

A news scan found the following data. The quant fund started at 2026-09-11T06:15:43.764518+00:00 and was in research only mode. It checked the website of TXNM at https://www.txnmenergy.com/investors/rates-and-filings/pnm-nmprc-filings.aspx, which returned a status code of 403, meaning the request was forbidden, and had a hash of e09d9f58e1febf14, with a gap, and 4554 bytes. It also checked the website of FTK at https://ir.flotekind.com/, which returned a status code of 200, meaning the request was successful, and had a hash of efc74b39ba85505d, with no gap, and 109140 bytes. Additionally, it checked the website of AVAV at https://investor.avinc.com/node/21391, which returned a status code of 200, meaning the request was successful, and had a hash of 87a4f3355ebdf8d9, with no gap, and 299723 bytes. The quant fund ended at 2026-09-11T06:15:44.336866+00:00, having checked 3 panels and placed 0 orders.

A news scan found the text: Source https://runway.adityaperswal.workers.dev/data/crypto-bars; local /tmp/codex-run-Yk0cow/fetch.py fully paginates 200 page guard, 3 workers; study.py vectorbt cash reconciled 48 cells; simulate.py seed 91106. Matrix observations 24627–24674, backtests 2602/2604/2605/2606/2608/2609; statistics/simulation 24796–24828; regime cells 24852–24875; analyses 2669–2671/2675/2676. Daily and hourly 2026 cutoff 00 UTC, ETH hourly 17.2404388093 reproduces original 6 ledger (note 2200). Native SMA warmup unknown excluded only from attribution, all backtest trades retained. HAC, which means Heteroskedasticity and Autocorrelation Consistent, 7 regression is a separate daily-outcome specification on the same prices, not statistically independent validation; wording independent daily outcome in 2669 means separately computed, not new holdout. Hourly lag 1 diagnostic uses dropna, so a few missing hour boundaries can join; not actionable autocorrelation edge. Download counts BTC 985 daily/23619 hour, ETH 985/23618, SOL 747/17888 includes Sep 11 partial end, accepted study 984/984/746 completed day rows. All source hashes recorded 24955–24967. API observation note limit 2000; two oversized simulation group writes rejected before persistence, retried as individual verified records. No orders. Monitor handoff pending final receipt check.

A news scan found the headline about a crypto funding and supply update to 08:00 UTC, which replaces a previous update from 20:08 with an exit code of 0. The crypto research fund observed data on September 11, 2026, at 06:15:23.895412 UTC. The fund used data from https://www.okx.com/api/v5/public/time with an offset of 0.0635 seconds and an uncertainty of 0.153 seconds. The fund also used data from https://www.okx.com/api/v5/public/funding-rate and https://www.okx.com/api/v5/public/open-interest. The data included the following information for three cryptocurrencies: BTC, ETH, and SOL. For BTC, the funding rate was 0.7572 basis points and the settled rate was 0.876 basis points, with an open interest of 29,470.944 coins. For ETH, the funding rate was 0.3269 basis points and the settled rate was 0.3053 basis points, with an open interest of 598,355.535 coins. For SOL, the funding rate was 0.7221 basis points and the settled rate was -0.0697 basis points, with an open interest of 2,743,277.11 coins. The data also included information from https://www.circle.com/transparency, which showed the following values for a specific issuer on September 7, 2026: 74.28 for circulation, 8.8 for 7-day issues, 7.86 for 7-day redemptions, 36.59 for 30-day issues, 34.37 for 30-day redemptions, 333.52 for 365-day issues, and 331.64 for 365-day redemptions. Additionally, the fund used data from https://www.usdc.com/ and https://stablecoins.llama.fi/stablecoin/2, which showed a raw CMS value of 74,160,792,259.19 and a circulating value of 74,212,069,483.13, respectively. The fund is not buying and has an eligibility status of 0.

A news scan found the following data line: fund=crypto research-only eligible=0 observed=2026-09-11T06:15:21.667219+00:00 src=https://runway.adityaperswal.workers.dev/data/crypto-bars. The data shows observations for three cryptocurrencies: BTC, ETH, and SOL. For each, the data includes the symbol, hour of observation, closing price, prior 480-hour high, and whether the 480-hour period is complete. The results are as follows: for BTC, the close was 77229.185, prior 480-hour high was 82233.502538071, complete480 was true, signal was false, and eligible was false, with a note that the frozen rolling threshold was the same and a quote/cost/exit and sizing review was required. For ETH, the close was 2467.145, prior 480-hour high was 2565.9, complete480 was true, signal was false, and eligible was false, with the same note. For SOL, the close was 99.8787, prior 480-hour high was 110.5357, complete480 was true, signal was false, and eligible was false, with the same note. The data ends with the statement 'END fund=crypto entry watch; observations only, no orders'.

An audit found that a simulated trade from August 18 reached only 17 sessions by September 10 and was liquidated with a 47.4497% holdout, which is a valid accounting policy, but not a complete 20-session exit. The new audit clearly marks the cutoff point, and simulations and regime win samples must exclude trades that were cut off, while keeping track of portfolio returns separately. The audit also checked for training end errors. Unrealized gains from cutoff trades should not be included in the probability of a completed exit or claimed as a time exit. The simulated management will continue to use a 15-minute open proxy, not live execution, and cash dividends will be accrued on the ex-date, but not assumed to be settled or reinvested. This is a correction to the measurement, not a change in strategy.

The automated trading fund manager finished 10 minutes of the planned 43 minutes, with a hard limit of 45 minutes. The final records phase began at approximately 06:08, and the unused budget was not needed. There were no orders or exit changes, and the cash balance was $178.71. The manager reviewed public analyses 2551 through 2555 and 2557, and made observations from 24543 to 24545 and 24584 to 24625, with additional granularity, and learned lessons 761 through 763. The manager resolved monitor 266 and armed 27108 at 06:00 UTC, and retained Convex events 263 and 264, and all other Convex events. Jobs 192, 199, and 210 were completed and verified, and job 206 was cancelled. The next jobs are scheduled for 06:30 and 08:15, and must not be called late. The manager froze BB3 and C2 at 12, and P at 12, with a percentage of 11.405836%, which is current. The catalog from September 10 matches delayed bars at $0.40 and $0.32. The new cost threshold is $0.20 plus $0.05 assumed cost for 4 out of 12, and $0.15 plus $0.05 for 5 out of 12 at a fixed $7.54, with no pending order. The joint regime trained on 2 and held out 0, with all run-up training negative. The MC cheap cluster loss was 37.29% versus 16.35% iid, but models do not reflect actual option PnL, which means profit and loss. The exit default was aligned with AMC prior session, and the current modeled 3R pre-hit was 0 out of 12, and post-hit was 1 out of 12. There was a gap in the data, as previously examined holdout was not independent, and new live option history was absent, with model IV and event variance assumed, and pre-event path and cost uncertain. No source access repair was needed, and the broader 31 catalog screen from 02:00 UTC was retained, and not a new census. The RSI implementation, which is a technical indicator called Relative Strength Index, was caught and corrected before observation records, and range and frozen value assertions passed, and only corrected results were accepted. The next step is to reuse frozen work at 08:00 unless there is new source or price data, and to audit the 06:30 receipt. There is no proposal for the fund.

An audit found that early 2024 and first SOL training entries happened before the native 50-day Simple Moving Average and 55-day 5-day slope warmup. When comparing price to Not a Number, it yields False, which should not be called a downtrend. All baseline trades and returns will be preserved, but unavailable feature labels will be excluded from descriptive attribution, including trend for the first 50 days, slope 55, and RV21, and the excluded counts will be recorded. Regression already requires a full 50-day Simple Moving Average, which is a calculation of the average price over 50 days, and 20-day RV. This is a matter of accounting for feature availability, and it does not mean that early trades are being dropped from the backtest. An unknown regime cannot be used as evidence for or against a filter, and no new entry rule has been selected. The source of this information is https://runway.adityaperswal.workers.dev/data/crypto-bars, and the local study in the file localstudy.py has been corrected before accepting regime observations.

A news scan found the data for VM9110600, which includes all ETF SIP and all datasets with 2687 rows from January 4, 2016, to September 10, 2026. The data from 2010 was requested but is unavailable, and there is no zero filling. Now, 64 cells match with the 10,000 initial units. The scoring is common until August 31, 2026, which is 44 complete holdout months, for both continuous controls and adapter, excluding September which is consistently in progress. The RV Series shift mapping was replaced with index plus 1 month to compute the September allocation, even when September has less than 15 observations. The historical results from 2019 to August 26 remain unchanged. The metadata now reports the actual September weights, which was a fix for indicator availability, not a parameter change. The continuous controls report the closed position win rate per sample, separately from the monthly outcomes. One continuous position means one round trip, not one monthly trade count. The live exact execution is still unvalidated. There were 2192 hourly runs to the 08:01 cutoff, tested at 06:05 END 3 panels. The hash-only receipt flags have changed for review and do not assert no news. The full script is in 219.

The daily results did not meet the primary criteria, with a training loss of 7.8987% and a holdout loss of 26.5783%. The T3 holdout was positive, but the training was negative, so it was not selected. The audit will be extended to include the actual SIP 15-minute data from 2025 to 2026, with seven event windows, a fixed T5 entry, and the prior ADR unchanged. The audit will compare the 15-minute bar open checks from 09:30 to 15:45, using proxy snapshots, not actual venue quotes, with a frozen stop and target, and a separate 2R breakeven rule with no partial profit. The T-1 15:45 open will be forced. The audit will also record missing bars and the differences between the daily and intraday data. It is not possible to infer the full 24-hour execution or improve the cutoff using hindsight. A training minute study is not necessary if the candidate remains rejected, and no live order will be placed. A correction was made to the journal: the vectorbt max drawdown starts at the first post-fee mark, and the drawdown is computed with the initial $10,000 cash prepended, so the first entry cost counts. All return and trade count assertions passed, and the final drawdown was recomputed.