Notes

A news scan found the following data: Measured on September 11 at 06:09 UTC, a trading analysis was done using historical data from 2024 to 2025 for training and from January 1, 2026, to September 11, 2026, as a holdout. The analysis used 365 days of path data with moving blocks of 1, 24, and 72 hours, a 20-day horizon, and 10,000 paths each. The analysis did not use trailing stops or channel and breakout conditioning. It used a trade bootstrap of 20,000 independent and identically distributed samples and a regression with 7 lags. The beta values were based on the intercept, trend, simple moving average of 50, high realized volatility, and cross values. The realized volatility was calculated over 20 days, annualized, and then square rooted over 365 days, with a training median. The analysis found the following results for the BTC symbol: block hours of 1, 10,000 paths, 8,757 blocks available, no trailing stops, a horizon of 480 hours, a risk percentage of 5.9137499154, a target up percentage of 20.5535946945, a net stop percentage of 6.5706388947, a stop percentage of 60.14, a target percentage of 4.08, a timeout percentage of 35.78, a mean net of -1.7933383413, a 5th percentile net of -7.5813274889, a 95th percentile net of 16.1416176424, a worst net of -10.7975429344, and a probability of loss of 69.24. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the following data: this is a list of events from an automated trading fund manager. The list includes four events: on 2025-09-25, 2026-01-13, 2026-03-24, and 2026-06-29. For each event, the data includes the entry price, which is the price at which the trade was entered, the exit price, which is the price at which the trade was exited, and the reason for the exit, which in all cases was a stop, meaning the trade was stopped due to a certain condition being met. The data also includes the net percentage change, which is the percentage change in the value of the trade, and the number of bars, which refers to the number of periods of time, in this case minutes, that the trade was open. The events were computed on 2026-09-11. The simulation used 20,000 paths and 10 trades, with a circular 2 event block, a seed of 91106, and only the first 15 minutes of trading were used for training. The fraction of the fund that was rebalanced was 0.54945, which is a one-share-sized fraction, and the simulation assumed a gap shock of 1/5% per trade and a -30% return. The data used minute bars from regular trading hours, which is a period of time when markets are open, and did not guarantee overnight or limit fill, which refers to the filling of trades at a specific price. The subsets of data were exploratory, meaning they were used to explore the data, and were not retuned, meaning they were not adjusted to fit the data. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=CNXC,SPY&feed=sip&adjustment=all

A news scan found the following data: the automated trading fund manager reported on four events. The events occurred on the following dates: 2026-06-29, 2025-01-15, 2025-03-26, and 2025-06-26. For each event, the manager reported whether the trade was a break-even trade, denoted as BE, which means the trade did not result in a significant loss or gain. The slippage_bp, or slippage in basis points, which is a measure of the difference between the expected price and the actual price of a trade, was also reported. The entry and exit prices were 24.36 and 22.8 for the first event, 43.31 and 43.51 for the second event, 43.54 and 41.43 for the third event, and 52.55 and 51.03 for the fourth event. The reason for exiting each trade was also given, with reasons being either a stop, which means a predetermined price was reached, or time, which means a predetermined time was reached. The net percentage change, or net_pct, was -6.9638 for the first event, -0.5378 for the second event, -5.7929 for the third event, and -3.8587 for the fourth event. The number of bars, or periods of time, for all hours and for regular trading hours, denoted as RTH, were also reported. The daily net percentage change and the weekday excess versus sessions were also given. The expected regular trading hours from five daily sessions and the missing regular trading hours were also reported. The data was computed on 2026-09-11. The simulation used 20,000 paths and 10 trades, with a circular 2-event block, a seed of 91106, and training on the first 15 trades only. The fraction of the fund rebalanced was 0.54945, which is a one-share-sized fraction. The simulation also assumed a gap shock of 1/5% per trade and a -30% return. The data used minute bars as a proxy for regular trading hours, with no guarantee of overnight or limit fill. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=CNXC,SPY&feed=sip&adjustment=all

A news scan found the following data line: a list of events for the CNXC fund. The events are as follows: on 2025-06-26, the fund entered at 52.55 and exited at 51.03 on 2025-06-18 at 10:45:00-04:00 due to a stop, with a net percentage change of -3.4733850179626713. On 2025-09-25, the fund entered at 54.18 and exited at 52.27 on 2025-09-24 at 09:45:00-04:00 due to a stop, with a net percentage change of -4.102402296889385. On 2026-01-13, the fund entered at 40.92 and exited at 39.22 on 2026-01-12 at 13:15:00-05:00 due to a stop, with a net percentage change of -4.727800242308689. On 2026-03-24, the fund entered at 31.25 and exited at 29.32 on 2026-03-19 at 09:30:00-04:00 due to a stop, with a net percentage change of -6.7372595198659795. The data was computed on 2026-09-11. The simulation used 20,000 paths and 10 trades, with a circular 2-event block, seed 91106, and training on the first 15 minutes only. The fraction of the fund rebalanced was 0.54945, which is a one-share-sized fraction today, not a fixed one-share fraction for the future. The simulation also assumed a 1/5% gap shock per trade and a -30% return. The data used minute bars during regular trading hours, with no guarantee of overnight or limit fill. The subsets were exploratory, with no retuning. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=CNXC,SPY&feed=sip&adjustment=all

A news scan found the headline 'CNXC CNXC91106 15min_event_audit batch4'. This is a list of events from an automated trading fund manager. The list includes four events: one on 2026-03-24, one on 2026-06-29, one on 2025-01-15, and one on 2025-03-26. Each event has details such as entry and exit prices, and the reason for the exit, which is either a stop or a time-based exit. A stop is a pre-set price level that triggers an exit when reached. The events also include metrics such as net percentage change and daily net percentage change. The data was computed on 2026-09-11 and is based on a simulation with 20,000 paths and 10 trades. The simulation used a circular approach with two event blocks and a seed of 91106. The fraction of the fund rebalanced is 0.54945, which is a one-share-sized fraction. The simulation also assumed a gap shock of 1/5% per trade and a -30% return. The data is based on minute bars during regular trading hours, which is a period when markets are open, and does not guarantee overnight or limit fill, which refers to the execution of trades at a specific price or better. The subsets of data are exploratory and have not been re-tuned, meaning they have not been adjusted for optimal performance. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=CNXC,SPY&feed=sip&adjustment=all

A news scan found the headline 'CNXC CNXC91106 15min_event_audit batch3'. This is a list of events from an automated trading fund manager. The events are from the following dates: 2025-03-26, 2025-06-26, 2025-09-25, and 2026-01-13. For each event, the manager recorded whether it was a break-even event, which is an event where the profit equals the loss, the slippage in basis points, which is a measure of the difference between the expected price and the actual price, the entry and exit prices, and the reason for exiting the trade. The reason for exiting was a stop, which means the trade was stopped when it reached a certain price. The manager also recorded the net percentage change and the number of bars, which are units of time, for all bars and for regular trading hours, or RTH. The daily net percentage change and the weekday excess versus sessions were also recorded. The expected RTH from five daily sessions and the missing RTH were also noted. The data was computed on 2026-09-11 and is based on a simulation with 20,000 paths and 10 trades, using a circular 2-event block, a seed of 91106, and training on only the first 15 events. The simulation also assumed a gap shock of 1/5% per trade and a -30% return. The data uses minute bars as a proxy for regular trading hours and does not guarantee overnight or limit fill. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=CNXC,SPY&feed=sip&adjustment=all

A news scan found the following data: this is a list of events from an automated trading fund manager. The events are from the following dates: 2026-01-13, 2026-03-24, 2026-06-29, and 2025-01-15. For each event, the data includes the entry price, which is the price at which the trade was entered, the exit price, which is the price at which the trade was exited, and the reason for the exit, which is either a stop, meaning the trade was stopped due to a certain condition, or time, meaning the trade was exited after a certain amount of time. The data also includes the daily entry difference, which is the difference between the entry price and the previous day's entry price, the net percentage change, which is the percentage change in the trade, and the number of bars, which refers to the number of time periods, or bars, of data used in the analysis. A bar is a unit of time, in this case, a minute. The data also includes the weekday expected RTH, which refers to the expected number of trading hours, or regular trading hours, in a weekday, and the missing RTH, which refers to the number of missing trading hours. The simulation was computed on 2026-09-11 and used 20,000 paths and 10 trades, with a circular 2 event block, seed 91106, and trained on the first 15 minutes of data only. The fraction of the fund rebalanced was 0.54945, which is a one-share-sized fraction, and the simulation assumed a gap shock of 1/5% per trade and a -30% return. The data used minute bars and a snapshot proxy, with no guarantee of overnight or limit fill. The subsets were exploratory, with no retune. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=CNXC,SPY&feed=sip&adjustment=all

A news scan found the headline 'CNXC CNXC91106 15min_event_audit batch1'. This is a list of events from an automated trading fund manager. The list includes four events that occurred on the following dates: 2025-01-15, 2025-03-26, 2025-06-26, and 2025-09-25. For each event, the details are as follows: the event date, whether the event was a break-even, or BE, event, the slippage in basis points, or bp, which is a measure of the difference between the expected and actual price of a trade, the entry and exit prices, the reason for the exit, the net percentage change, and the number of bars, which are periods of time, in all and regular trading hours, or RTH. The events had the following details: on 2025-01-15, the entry price was 43.31, the exit price was 43.51, and the reason for the exit was time, on 2025-03-26, the entry price was 43.54, the exit price was 41.43, and the reason for the exit was a stop, on 2025-06-26, the entry price was 52.55, the exit price was 51.03, and the reason for the exit was a stop, and on 2025-09-25, the entry price was 54.18, the exit price was 52.27, and the reason for the exit was a stop. The simulation was computed on 2026-09-11 and used 20,000 paths and 10 trades, with a seed of 91106 and a train of N15 only, and assumed a gap shock of 1/5% per trade and a -30% return. The simulation used minute bars and a regular trading hours snapshot proxy, with no overnight or limit fill guarantee, and the subsets were exploratory, with no retune. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=CNXC,SPY&feed=sip&adjustment=all

A news scan found the following data: this is a simulation batch for CNXC, specifically CNXC91106, computed on 2026-09-11. The simulation used 20,000 paths and 10 trades, with a circular 2-event block, seed 91106, and training on the last 15 data points only. The fraction of the fund rebalanced varied, with 0.5494505494505495 being the fraction for one-share-sized trades today, which is not a fixed one share for future trades. The simulation assumed a gap shock of 1/5% per trade and a -30% return. It used minute bars as a proxy for real-time market data, taken during regular trading hours, with no guarantee of overnight or limit fill. The results are as follows: there were four scenarios tested, with the following results: the first scenario had a fraction of 0.5494505494505495, an assumed gap probability of 0.05, a gap return of -0.3, a median percentage change of -8.243819593859797, a probability of loss of 0.8098, a probability of a 20% drawdown of 0.2871, a 5th percentile of -31.804881693416263, and a mean percentage change of -10.313187895806946. The second scenario had a fraction of 1, an assumed gap probability of 0, a gap return of -0.3, a median percentage change of -5.44435873243137, a probability of loss of 0.6938, a probability of a 20% drawdown of 0.10435, a 5th percentile of -20.60249367590513, and a mean percentage change of -4.741991450280178. The third scenario had a fraction of 1, an assumed gap probability of 0.01, a gap return of -0.3, a median percentage change of -6.926521592359109, a probability of loss of 0.72395, a probability of a 20% drawdown of 0.19155, a 5th percentile of -33.294896276596965, and a mean percentage change of -7.558949768271352. The fourth scenario had a fraction of 1, an assumed gap probability of 0.05, a gap return of -0.3, a median percentage change of -14.872146693414546, a probability of loss of 0.81545, a probability of a 20% drawdown of 0.46585, a 5th percentile of -53.020321311503615, and a mean percentage change of -17.98382412954314. Note that these subsets are exploratory and no retuning was done. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=CNXC,SPY&feed=sip&adjustment=all

A news scan found the following simulation results for CNXC: the simulation was computed on 2026-09-11 and consisted of 20,000 paths and 10 trades. The results were as follows: a fraction of 0.25 with an assumed gap probability of 0.01 had a gap return of -0.3, a median percentage change of -1.6272, a probability of loss of 0.7082, a probability of a 20% drawdown of 0.0001, a 5th percentile of -8.7262, and a mean percentage change of -1.9267. Another fraction of 0.25 with an assumed gap probability of 0.05 had a gap return of -0.3, a median percentage change of -3.6943, a probability of loss of 0.8049, a probability of a 20% drawdown of 0.0122, a 5th percentile of -15.2886, and a mean percentage change of -4.7956. A fraction of 0.5495 with an assumed gap probability of 0 had a gap return of -0.3, a median percentage change of -2.8702, a probability of loss of 0.6841, a probability of a 20% drawdown of 0, a 5th percentile of -11.7556, and a mean percentage change of -2.6157. Lastly, a fraction of 0.5495 with an assumed gap probability of 0.01 had a gap return of -0.3, a median percentage change of -3.6863, a probability of loss of 0.7163, a probability of a 20% drawdown of 0.0609, a 5th percentile of -18.6959, and a mean percentage change of -4.2054. The simulation used 20,000 paths, 10 trades, circular 2-event blocks, a seed of 91106, and trained on the last 15 days only, with a fraction of the fund rebalanced, which is 0.5495, a one-share-sized fraction today, but not a fixed one-share fraction for the future. The simulation also assumed gaps of 1/5% per trade and a -30% return, used minute bars as a proxy for regular trading hours, and did not guarantee overnight or limit fill. The subsets were exploratory and did not involve retuning. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=CNXC,SPY&feed=sip&adjustment=all

A news scan found the following simulation data for CNXC: the simulation was computed on 2026-09-11 and used 20,000 paths and 10 trades. The simulation was set up with circular 2 event blocks, a seed of 91106, and training on the last 15 days only. The fraction of the fund that was rebalanced was 0.54945, which is the size of one share today, but this fraction is not fixed at one share for future trades. The simulation assumed a gap shock of 1/5 percent per trade and a return of -30 percent. The data used was from minute bars during regular trading hours, and there is no guarantee of overnight or limit fill. The subsets used were exploratory and no retune was done. The results of the simulation were as follows: when the fraction was 0, the assumed gap probability was 0, the gap return was -0.3, the median percent change was 0, the probability of loss was 0, the probability of a 20 percent drawdown was 0, the 5 percent percentile was 0, and the mean percent change was 0. When the fraction was 0 and the assumed gap probability was 0.01, the results were the same. When the fraction was 0 and the assumed gap probability was 0.05, the results were also the same. When the fraction was 0.25 and the assumed gap probability was 0, the median percent change was -1.2674549071149732, the probability of loss was 0.67785, the probability of a 20 percent drawdown was 0, the 5 percent percentile was -5.483365714790834, and the mean percent change was -1.1931573400440427. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=CNXC,SPY&feed=sip&adjustment=all

A news scan found the following data: the CNXC91106 regime subsets batch4 results are given in two parts. The first part is for the training window, where the return was -4.866383262420877, the drawdown was 13.547220169438102, and the win rate was 50. The sample size was 14, the mean percentage change was -0.2872928442103454, the average win percentage was 3.0930941426139076, and the average loss percentage was 3.667679831034598. The number of targets was 1, the worst percentage change was -5.074661691100624, and the mean excess percentage per trade was -0.9629721693811664. The second part is for the holdout window, where the return was -26.57828415023204, the drawdown was 27.90600877346402, and the win rate was 0. The sample size was 7, the mean percentage change was -4.29254621439878, the average win percentage was 0, and the average loss percentage was 4.29254621439878. The number of targets was 0, the worst percentage change was -6.983554867698272, and the mean excess percentage per trade was -4.03538364253143. These results were computed on 2026-09-11. The simulation used 20,000 paths and 10 trades, with a circular 2-event block and a seed of 91106. The training set used only the first 15 data points, and the fraction of the fund rebalanced was 0.54945, which is a one-share-sized fraction today, but not a fixed one-share fraction for the future. The simulation also assumed a gap shock of 1/5% per trade and a -30% return. The data used was based on minute bars during regular trading hours, with no guarantee of overnight or limit fill. The subsets used were exploratory, with no retuning. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=CNXC,SPY&feed=sip&adjustment=all

A news scan found the following data: the regime subsets batch3 for CNXC91106 were computed on 2026-09-11. The subsets are as follows: for both_up in the train window, the return was -3.778565632545124, the drawdown was 3.778565632545128, the win rate was 0, the sample size was 1, the mean percentage was -3.778565632545128, the average win percentage was 0, the average loss percentage was 3.778565632545128, the targets were 0, the worst percentage was -3.778565632545128, the mean excess percentage per period was -6.01715878843867, and the ambiguous count was 0. For both_up in the holdout window, the return was -7.479860271176257, the drawdown was 9.152952029399053, the win rate was 0, the sample size was 3, the mean percentage was -2.542483361225693, the average win percentage was 0, the average loss percentage was 2.542483361225693, the targets were 0, the worst percentage was -3.9785628508407944, the mean excess percentage per period was -2.095028006831179, and the ambiguous count was 0. For post_webhelp in the train window, the return was 8.058692938003205, the drawdown was 5.653697474038477, the win rate was 75, the sample size was 4, the mean percentage was 2.002492604393577, the average win percentage was 3.179002928432243, the average loss percentage was 1.527038367722422, the targets were 1, the worst percentage was -1.527038367722422, the mean excess percentage per period was 0.5525622879772124, and the ambiguous count was 0. For post_webhelp in the holdout window, the return was -26.57828415023204, the drawdown was 27.90600877346402, the win rate was 0, the sample size was 7, the mean percentage was -4.29254621439878, the average win percentage was 0, the average loss percentage was 4.29254621439878, the targets were 0, the worst percentage was -6.983554867698272, the mean excess percentage per period was -4.03538364253143, and the ambiguous count was 0. The simulation used 20,000 paths and 10 trades, with circular 2-event blocks, a seed of 91106, training on the last 15 only, and a fraction of the fund rebalanced, which is 0.54945, a one-share-sized fraction today, not a fixed one share in the future. The simulation also included gap shocks of 1/5% per trade and a -30% return assumed, using minute bars and a regular trading hours snapshot proxy, with no overnight or limit fill guarantee. The subsets are exploratory and were not retuned. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=CNXC,SPY&feed=sip&adjustment=all

A news scan found the following data: the automated trading fund manager computed results on 2026-09-11 for the CNXC91106 regime subsets batch 2. The results are as follows: for the market up subset in the training window, the return was -8.550517336843969, the drawdown was 17.7857651475575, the win rate was 40, the sample size was 10, the mean percentage change was -0.8272009822618664, the average win percentage was 3.0416766032800635, the average loss percentage was 3.4064527059564864, the number of targets was 1, the worst percentage change was -4.523321069797714, the mean excess percentage per trade was -1.3956115708156858, and there were 0 ambiguous trades. For the market up subset in the holdout window, the return was -22.550335029382104, the drawdown was 23.950899236475365, the win rate was 0, the sample size was 6, the mean percentage change was -4.141182000545395, the average win percentage was 0, the average loss percentage was 4.141182000545395, the number of targets was 0, the worst percentage change was -6.983554867698272, the mean excess percentage per trade was -3.4158552331433256, and there were 0 ambiguous trades. For the market down subset in the training window, the return was 0.7127514066027288, the drawdown was 7.15102257989032, the win rate was 60, the sample size was 5, the mean percentage change was 0.21249407493141081, the average win percentage was 3.161650861725699, the average loss percentage was 4.211241105260022, the number of targets was 0, the worst percentage change was -5.074661691100624, the mean excess percentage per trade was 0.27418658361083814, and there were 0 ambiguous trades. For the market down subset in the holdout window, the return was -5.200731497519082, the drawdown was 6.187446397008822, the win rate was 0, the sample size was 1, the mean percentage change was -5.200731497519085, the average win percentage was 0, the average loss percentage was 5.200731497519085, the number of targets was 0, the worst percentage change was -5.200731497519085, the mean excess percentage per trade was -7.752554098860054, and there were 0 ambiguous trades. The simulation used 20,000 paths and 10 trades, with circular 2-event blocks, a seed of 91106, training on the last 15 only, and a fraction of the fund rebalanced, which is 0.54945, representing one share-sized fraction today, not a fixed one share in the future. The simulation also assumed a gap shock of 1/5% per trade and a -30% return. The data used was minute bars, with a regular trading hours snapshot proxy, and no guarantee of overnight or limit fill. The subsets were exploratory, with no retune. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=CNXC,SPY&feed=sip&adjustment=all

A news scan found the following data: a list of regime subsets for CNXC, computed on 2026-09-11. The subsets are as follows: for the stock_up subset in the train window, the return was -2.4767471191366712, the drawdown was 8.406733611769967, the win rate was 50, the sample size was 4, the mean percentage change was -0.5446336566430732, the average win percentage was 3.3373463485367294, the average loss percentage was 4.426613661822875, there were 0 targets, the worst percentage change was -5.074661691100624, and the mean excess percentage per trade was -0.8187460263021529. For the stock_up subset in the holdout window, the return was -12.291584319601897, the drawdown was 13.877663067772406, the win rate was 0, the sample size was 4, the mean percentage change was -3.2070453952990405, the average win percentage was 0, the average loss percentage was 3.2070453952990405, there were 0 targets, the worst percentage change was -5.200731497519085, and the mean excess percentage per trade was -3.509409529838398. For the stock_down subset in the train window, the return was -5.559661499722843, the drawdown was 15.096948141248257, the win rate was 45.45454545454545, the sample size was 11, the mean percentage change was -0.45736407467175616, the average win percentage was 2.9953932602447786, the average loss percentage was 3.3346618537688686, there was 1 target, the worst percentage change was -4.523321069797714, and the mean excess percentage per trade was -0.8463816986267324. For the stock_down subset in the holdout window, the return was -16.28885862297369, the drawdown was 16.28885862297369, the win rate was 0, the sample size was 3, the mean percentage change was -5.739880639865099, the average win percentage was 0, the average loss percentage was 5.739880639865099, there were 0 targets, the worst percentage change was -6.983554867698272, and the mean excess percentage per trade was -4.736682459455474. The simulation used 20,000 paths and 10 trades, with a circular 2-event block, a seed of 91106, and only the first 15 trades in the train set. The fraction of the fund rebalanced was 0.54945, which is a one-share-sized fraction today, but not a fixed one-share fraction for the future. The simulation also assumed a gap shock of 1/5% per trade and a -30% return. The data used was minute bars, with a regular trading hours snapshot proxy, and no guarantee of overnight or limit fill. The subsets were exploratory, with no retune. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=CNXC,SPY&feed=sip&adjustment=all

A list of entry exclusions for CNXC, also known as CNXC91106, includes the following dates: an event on 2023-06-28 with an entry on 2023-06-13, an event on 2023-09-27 with an entry on 2023-09-13, an event on 2024-06-26 with an entry on 2024-06-11, an event on 2025-01-15 with an entry on 2024-12-30, an event on 2025-06-26 with an entry on 2025-06-11, and an event on 2025-09-25 with an entry on 2025-09-11. This information was computed on 2026-09-11 using data from saved studies, including intraday studies. The data used includes price bars up to September 10, financial reports from May 31 that were reported on June 29, and valuation at the September 10 price using May shares. The forward scenarios are assumptions and not measured probabilities. The ledger is based on the catalyst's 3 closed trades, which does not reflect CNXC's performance. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=CNXC,SPY&feed=sip&adjustment=all

A news scan found the trade ledger for CNXC91106. The ledger includes the following trades: for MIRM, the return percentage was -0.4393212687920589, the R value was -0.06982703395259335, and the profit and loss was -0.2855148925559953. For IONS, the return percentage was -2.8424202127659504, the R value, which is a measure of risk, was -0.4110576923076911, and the profit and loss was -1.8472888958399931. For RARE, the return percentage was -45.860061919504645, the R value was -6.440347826086957, the profit and loss was -29.804454238440957, and the stop slippage percentage, which is the percentage of loss due to selling at a worse price than expected, was -41.70933333333333. This data was computed on September 11, 2026, from a study using price bars through September 10 and financial reports from May 31 that were released on June 29, with valuations based on the September 10 price and the number of shares from May. The forward scenarios are assumptions and not based on measured probabilities. The ledger only includes three closed trades from the catalyst and does not reflect the overall performance of CNXC. runway:query_trades?fund=catalyst

A news scan found the headline is not available, but a data line was found with the following information: the company CNXC has the following financial data as of September 11, 2026: H1 CFO is 174.671 million, H1 capex is 102.076 million, H1 free cash flow, or FCF, which is the amount of cash a company has after paying expenses, is 72.595 million, H1 prior FCF is 131.534 million, and H1 FCF year over year percentage change is -44.80894673620508. Q2 FCF is 209.717 million, Q2 adjusted FCF, which is FCF with some adjustments, is 242.324 million, and Q2 factoring adjustment, which is an adjustment for the company's accounts receivable, is 32.607 million. H1 adjusted FCF is 97.711 million, FY adjusted FCF midpoint is 640 million, required H2 adjusted FCF is 542.289 million, required quarterly H2 is 271.1445 million, and H2 H1 required ratio is 5.549927848451044. H1 interest coverage, which is a measure of a company's ability to pay interest on its debt, is 1.4922833371689994, Q2 interest coverage is 1.4017245938243676, cash is 255.566 million, net debt carrying is 4329.308 million, market cap using May shares is 1643.301 million, simplified enterprise value, or EV, which is a measure of a company's total value, is 5972.6089999999995 million, EV annualized H1 adjusted EBITDA, which is a measure of a company's profitability, is 4.293406282168509, price adjusted EPS guide midpoint is 2.4534302589731944, H1 GAAP operating profit is 213.98 million, H1 interest is 143.391 million, and H1 adjusted EBITDA is 695.556 million. This data was computed on September 11, 2026, from saved study.py/intraday_study.py, using price bars through September 10, financials reported on June 29 for the period ending May 31, and valuation at the September 10 price using May shares. Forward scenarios are assumptions, not measured probabilities, and the ledger is based on the catalyst's 3 closed trades, not CNXC performance. https://ir.concentrix.com/news/news-details/2026/Concentrix-Reports-Second-Quarter-2026-Results/default.aspx

A performance report for the TLT symbol using a constant model on a test sample shows a return of -6.074126769098475 percent. The test had a slippage of 15 basis points, which is a measure of the difference between expected and actual trade prices, and a c_multiplier of 1. The drawdown, or largest decline in value, was 20.392947490245728 percent. The compound annual growth rate, or CAGR, was -1.7054854955885768 percent. The Sharpe ratio, a measure of risk-adjusted return, was -0.07057406336361718. Annualized volatility, or ann_vol, was 12.791546305935785 percent. Over 918 sessions and 44 months, the strategy had a win rate of 0 percent, with 1 trade, and 54.54545454545454 percent of months being positive. The mean trade return was -6.334978970680792 percent, with an average loss of 6.334978970680792 percent. Mean exposure, or the average amount of capital used, was 0.8983189139766774. The test started on 2023-01-03 and ended on 2026-08-31, with 45 orders. This report is based on a Sep 11 measurement, using a frozen 2230 SIP, or systematic investment plan, with a fee of 1 basis point, and fractional and normalized daily proxy data. https://runway.adityaperswal.workers.dev/data/stock-bars

A news scan found the following data: a computed result from September 11, 2026, based on a saved study from intraday_study.py, shows the following numbers: the target rate is 0.06666666666666667, the stop rate is 0.4666666666666667, and the time rate is 0.4666666666666667. The training half Kelly capital is 0, the training p is 0.4666666666666667, the training win percentage is 3.0930941426139076, and the training loss percentage is 3.60764980578237. This data is based on price bars up to September 10, financial reports from May 31 that were released on June 29, and valuation at the September 10 price using the number of shares from May. The forward scenarios are assumptions, not measured probabilities. The ledger is based on 3 closed trades from a catalyst, not the performance of CNXC. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=CNXC,SPY&feed=sip&adjustment=all

A news scan found the following data line: a regression analysis of CNXC, specifically CNXC91106, was computed on 2026-09-11 from saved study.py/intraday_study.py. The analysis used 15 data points and considered the following features: momentum over 20 periods, moving average distance, realized volatility over 20 periods, market bull indicator, and net return. The correlation between these features is shown in a 5x5 matrix. The beta per training standard deviation percent per point is also reported for each feature. The train R-squared value is 0.2252750526462337, indicating the proportion of variance in the dependent variable that is predictable from the independent variables. The holdout root mean squared error percent per point is 3.184007569082693, and the constant root mean squared error percent per point is 4.4002834853301716. The analysis used price bars through September 10, financials reported on June 29 for the period ending May 31, and valuation at the September 10 price using the number of shares as of May. Forward scenarios are assumptions and not measured probabilities. The ledger is based on 3 closed trades from a catalyst, not CNXC's performance. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=CNXC,SPY&feed=sip&adjustment=all

A news scan found the following data: the automated trading fund manager computed confidence intervals on September 11, 2026, from a saved study. The study used price bars through September 10 and financials reported on June 29 for May 31, with valuation at the September 10 price using May shares. The results are for two time periods: training and holdout. For the training period, the mean percentage change was -0.48063596319744073, the 95% confidence interval was between -2.2152594036925675 and 1.2931710935481127, the median percentage change was -1.527038367722422, and the number of data points was 15. For the holdout period, the mean percentage change was -4.29254621439878, the 95% confidence interval was between -5.773291973189492 and -3.0385240044122908, the median percentage change was -3.9785628508407944, and the number of data points was 7. Forward scenarios are assumptions, not measured probabilities. The ledger is based on the fund manager's 3 closed trades, which are not the same as CNXC's performance. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=CNXC,SPY&feed=sip&adjustment=all

A performance report for the TLT symbol using a constant model on a train sample shows the following results: the return was -10.454630108866258 percent, the drawdown was 40.62901185018851 percent, and the Compound Annual Growth Rate was -2.722862225194078 percent. The Sharpe ratio, a measure of risk, was -0.09775357043570017, and the annual volatility was 15.6837727494406 percent. Over 1008 sessions and 48 months, there was 1 trade, with a win rate of 0 percent and 45.83333333333333 percent of months being positive. The mean trade was -10.621922231295008 percent, with an average loss of 10.621922231295008 percent. The mean exposure was 0.8982587185197496, and there were 49 orders. The test started on 2019-01-02 and ended on 2022-12-30. A news scan found the headline 'Sep11measurement' and other notes mentioned 'frozen2230, SIP/all, fee1bp, fractionalnormalized, dailyproxy'. https://runway.adityaperswal.workers.dev/data/stock-bars

A technical analysis of CNXC, also known as CNXC91106, was computed on 2026-09-11 from saved studies. The analysis used price bars through September 10 and financial reports from May 31, which were released on June 29. The valuation was calculated using the September 10 price and the number of shares from May. The results include various technical indicators: the simple moving average, or average price, over 20 days was 27.6525, over 50 days was 25.5096, and over 200 days was 29.8007. The average daily range, or typical price swing, over 20 days was 1.8721, and the average true range, or total price movement, over 14 days was 2.0541. The relative strength index, or RSI, which measures price momentum, was 47.6074. Other indicators include the momentum percentage over 20 days, which was 10.1591, and the realized volatility percentage over 20 days, which was 72.3670. The dollar volume, or total value of trades, over 20 days was 35,515,338.06. The worst gap percentage, or largest sudden price move, was -21.4830. The analysis also considered the performance of the SPY index, with a closing price of 757.83 and a 200-day simple moving average of 711.4916. The hypothetical stop loss, or selling price to limit losses, was 25.1279, and the hypothetical target, or selling price to take profits, was 32.6164. The maximum number of whole shares that could be bought was 1, and the potential loss per share as a percentage of the total account was 3.1728. The net risk-reward ratio, which compares potential gains to potential losses, was 2.6806. Forward scenarios are assumptions and not measured probabilities. The ledger is based on the catalyst's 3 closed trades, not CNXC's performance. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=CNXC,SPY&feed=sip&adjustment=all

A performance report for the stock symbol TLT shows the following results: the model used was constant, the sample was a test, and the slippage in basis points was 5. The return was -5.8979 percent, the drawdown was 20.3916 percent, and the compound annual growth rate was -1.6549 percent. The Sharpe ratio, a measure of risk, was -0.0666, and the annual volatility was 12.7887 percent. There were 918 sessions, 44 months, and 1 trade, with a win rate of 0 percent. The average trade loss was 6.1567 percent, and the mean exposure was 0.8983. This report started on January 3, 2023, and ended on August 31, 2026. A news scan found the note: Sep11 measurement; frozen2230, SIP/all, fee1bp, fractional normalized, daily proxy. https://runway.adityaperswal.workers.dev/data/stock-bars