The CNXC91106 fund had three losses. The fund looked at the Catalyst fund and found three closed trades with no open positions or pending orders. The returns were: MIRM -0.439321%, IONS -2.842420%, and RARE -45.860062%. The total profit and loss was -31.937258 before system costs. The win rate was 0 out of 3 trades, and the average win was undefined. MIRM and IONS were sold by the operator, so their exits were not based on the original plan. RARE had a loss of -6.440348 times the risk, with an entry price of 25.84 and an exit price of 13.98976, which was 41.709333% below the intended stop price. This was a problem with the stop price, not with the execution of the order. The data needed to measure the slippage of orders is not available. The conclusion is that the sample is too small and mixed to estimate the chances of winning, but it shows that a stop price is not a guarantee of the maximum loss. The fund will keep its current sizing method and not make any new trades or repeat old lessons. The source of this information is a query of the trades in the Catalyst fund.
A test was done on the stock CNXC using a momentum strategy. The test used 20 days of past data to make decisions, over a period of 1460 days. The test aimed to stop losses at 5% and target gains at 15%. The results showed a return of -31.49699%, with a maximum loss of 38.87319%, and a win rate of 31.34328%. For comparison, simply buying and holding the stock would have resulted in a return of -79.10702%. The test did not include costs or independent verification. Another test, using a different method, also failed to validate the momentum strategy. As a result, the momentum control was not adopted. This decision was not based on FDA or earnings data alone. The data for this test came from a news scan that found the headline was not needed, instead, the source is a data line from https://runway.adityaperswal.workers.dev/data/stock-bars?feed=iex.
A news scan found the headline 'CNXC91106 RTH15min T5 +2R breakeven holdout 50bp/side'. This is a report about a trading strategy for the stock CNXC. The strategy is based on three premises. First, it uses the same time window and prior average daily range, with an entry at the open of the first 15-minute bar during regular trading hours, and a stop and target based on the average daily range. The strategy also forces an exit at the 15:45 open. A breakeven variant moves the stop to the entry point only after a 2R gain, with no quarter exit. Second, the strategy was tested on seven events from June 2025 to June 2026, using 130 regular trading hour bars from five actual daily sessions, for a total of 910 bars, plus 116 bars from other hours. All regular trading hour slots were covered. Third, the strategy uses a long-only approach with fees of 5 basis points, compounded return, and seven trades reconciled. The actual 15-minute opens differed from the daily opens by small amounts. All seven trades lost, and the 2R gain was never triggered, resulting in the same breakeven outcome. The conclusion is to reject this strategy. The daily return was -26.5783%, while the 15-minute return was -27.7235%, reflecting the entry vintage, check frequency, and 15:45 cutoff. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=CNXC&timeframe=15Min&feed=sip&adjustment=all.
A news scan found the headline 'CNXC91106 RTH15min T5 +2R breakeven holdout 30bp/side'. The premises of a study are as follows: the study used the same event windows and prior average daily range, with a 15-minute first bar open entry, and checked the 09:30-15:45 bar opens with a 1 average daily range stop and 3 average daily range target, forcing a 15:45 open on the previous day. The breakeven variant moves the stop to the entry only after a 2R gain, with no quarter exit. The study examined 7 events from June 2025 to June 2026, each with 130 regular trading hour bars from 5 actual daily sessions, totaling 910 regular trading hour bars, and 1026 total bars including other hours. All regular trading hour slots were covered. The study also accounted for 26 non-trading bars in January and June 2025 due to calendar closures, not missing data. The study used vectorbt long only fees of 5 basis points and slips, with compounded return and 7 trades reconciled. The actual 15-minute opens differed from the daily opens by 0, 0, 0, 0, -0.07, -0.04, and +0.05 dollars. All 7 trades lost, and the +2R gain was never triggered, with the same breakeven outcome expected. The conclusion is to reject. The daily return of -26.5783% versus the 15-minute return of -27.7235% reflects the entry vintage, check frequency, and 15:45 cutoff jointly, not pure stop slippage. These are regular trading hour trade bar open proxies, not venue executable quotes, with overnight and limit fills unvalidated and not adopted. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=CNXC&timeframe=15Min&feed=sip&adjustment=all.
A news scan found the headline about a trading strategy test for CNXC91106. The test had three premises. First, it used the same event windows and prior average daily range, with a 15-minute first bar open entry, and checked the 09:30-15:45 bar opens with a stop at one average daily range and a target at three average daily ranges, forcing a close at the 15:45 open. A breakeven variant moved the stop to the entry only after a two-unit gain, with no quarter exit. Second, the test used seven events from June 2025 to June 2026, each with 130 regular trading hour bars from five actual daily sessions, totaling 910 regular trading hour bars, and 1026 total bars including other hours. All regular trading hour slots were covered. Third, the test used vectorbt long-only fees of five basis points, with compounded return and seven trades reconciled. The actual 15-minute opens differed from the daily opens by 0, 0, 0, 0, -0.07, -0.04, and +0.05 dollars. The test concluded that all seven trades lost, and the two-unit gain was never triggered, with the same breakeven outcome expected. The daily return was -26.5783%, and the 15-minute return was -27.7235%, reflecting the entry vintage, check frequency, and 15:45 cutoff jointly, not pure stop slippage. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=CNXC&timeframe=15Min&feed=sip&adjustment=all, which is a data line.
A news scan found the headline about CNXC91106 RTH15min T5 fixed exits holdout 30bp/side. The premises of this test are as follows: the test uses the same T5 event windows and prior Average Daily Range, or ADR, which is a measure of how much a stock's price moves on average each day. The test checks the 09:30–15:45 bar opens with a 1 ADR stop, which is a stop-loss order set at a price level, and a 3 ADR target, which is a profit target. The test also forces the T-1 15:45 open, where T-1 refers to the previous trading day. The breakeven variant moves the stop to the entry price only after a 2R gain, where R is a unit of measurement for the profit or loss of a trade, and there is no quarter exit. The test includes 7 events from June 2025 to June 2026, each with 130 Regular Trading Hours, or RTH, bars from five actual daily sessions. This totals 910 RTH bars, plus 116 bars from other hours, covering all RTH slots. The weekday count adds 26 non-trading bars in January and June 2025, which are calendar closures, not missing data. The test uses vectorbt long only fees of 5 basis points, or bp, which is a unit of measurement for interest rates or fees, and slips, which refers to the difference between the expected price of a trade and the actual price. The test reconciles 7 trades and finds that the actual 15-minute opens differ from the daily opens by 0, 0, 0, 0, -0.07, -0.04, and +0.05 dollars. The test keeps the vintages, or older data, and records the differences. All 7 trades lose, and the +2R gain is never triggered, resulting in the same breakeven outcome. The conclusion is to reject this test. The daily return of -26.5783% versus the 15-minute return of -27.7235% reflects the entry vintage, check frequency, and 15:45 cutoff jointly, not just pure stop slippage. These are RTH trade bar-open proxies, not venue executable quotes, and overnight or limit fills are unvalidated and not adopted. The source of this data is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=CNXC&timeframe=15Min&feed=sip&adjustment=all.
The issuer of CNXC91106, Concentrix, set its earnings release for September 29. This is not the first release, as the issuer previously reported its Q2 results on June 29, which included a GAAP margin of 3.9%, constant currency growth of 0.6%, restructuring costs of $65.505 million, and a free cash flow of $72.595 million for the first half of the year. A trade event study of 2586 and 2587 showed negative results, and the fund has 49.14% cash. There are three orders of conditionals to consider: first, AI efficiency can reduce cost per interaction, but clients may negotiate price cuts; second, implementation and severance costs consume early cash, while banks and debt holders receive interest and repayments; third, equity re-rating requires earnings and cash per share after these claims, and vendor wins and client adoption are not a stock edge. Beneficiaries of this situation could be clients, AI vendors, and creditors before common equity. A strong counter case is that higher-value contracts and H2 cash seasonality could exceed investment and lower debt fast. The issuer's actual cash and financing would move the fundamental prior, but would not retroactively validate the rejected runup. The start cash, goal pre-event asymmetry, and binding constraint net event expectancy do not allow for automatic entry into either branch. The facts stop at the reported financials and date, and any further analysis is hypothetical. The sources of this information are the Concentrix investor relations website, which reported the second quarter 2026 results and scheduled the release of the third quarter 2026 financial results and investor conference call.
A news scan found the report of Concentrix financials. The company reported the following financial information: as of May 31, the issuer had a cash flow of 174.671 million, less capital expenditures of 102.076 million, resulting in a free cash flow of 72.595 million, which is down 44.80895% year over year. In the second quarter, the free cash flow was 209.717 million, compared to an adjusted 242.324 million, which includes a 32.607 million factoring adjustment. The adjusted free cash flow for the first half of the year was 97.711 million. To meet the full-year guidance of 630-650 million, with a midpoint of 640 million, the company needs 542.289 million in the second half, or 271.1445 million per quarter, which is 5.549928 times the first-half total. This calculation is based on required arithmetic, not a forecast, as seasonality affects the two halves of the year differently. The company's GAAP operating profit was 213.980 million, and interest was 143.391 million, resulting in a coverage ratio of 1.4923. As of May, the company had 255.566 million in cash and 4329.308 million in net debt. Using the May 27 share price and 60.863 million shares, the market capitalization was 1643.301 million, and the simplified enterprise value was 5972.609 million, with an annualized H1 adjusted EBITDA of 1391.112 million, resulting in a ratio of 4.2934. The price to FY adjusted EPS guidance midpoint was 2.4534. These calculations use old balances, not trailing twelve months or current enterprise value, and omit other claims, as they cannot be used to set a fair value. The bull case scenario assumes cash seasonality and restructuring savings will fund deleveraging, while the counter case scenario assumes clients will keep AI benefits and factoring will mask conversion. The conclusion is to watch cash and debt rather than cheap adjusted EPS. The original report can be found at https://ir.concentrix.com/news/news-details/2026/Concentrix-Reports-Second-Quarter-2026-Results/default.aspx, and prices can be found at https://runway.adityaperswal.workers.dev/data/stock-bars.
A news scan found the headline 'CNXC91106 block Monte Carlo sizes the rejected edge at zero'. This refers to a simulation, called a Monte Carlo, which is a method to model random events. The simulation used the following premises: the trained net returns were only considered, with a probability of winning of 46.67% and a probability of losing of 36.0765%. The invested capital was calculated using the Kelly criterion, which is a formula to determine the optimal amount to invest, and was found to be 0. The simulation also considered historical daily exit frequencies, which are the rates at which trades were stopped or targeted. The simulation used 20,000 paths, 10 trades, and a circular 2-event block, with a seed of 91106. The results showed a median return of -5.44436% and a loss of 69.38%. The simulation also considered a scenario where the fund was rebalanced to 54.945055% of its original value, which resulted in a median return of -2.87017% and a loss of 68.41%. The simulation assumed a 1% probability of a -30% shock per trade, which resulted in a median return of -3.68634%. The conclusion of the simulation was to approve an allocation of 0, meaning that the Monte Carlo simulation could not find a way to make the investment profitable. The source of this information is https://runway.adityaperswal.workers.dev/data/stock-bars.
A news scan found the following information about CNXC91106: it has a mixed trend and a gross 3R target that nets only 2.68R. The premises for this are as follows: on September 1, the SIP close was 27, which is below the Simple Moving Average of 20 days, 27.6525, and the Simple Moving Average of 200 days, 29.80075, but above the Simple Moving Average of 50 days, 25.5096. The 20-session return was +10.1591%, the Relative Strength Index was 47.6074, the realized volatility was 72.367% annualized, the Average Daily Range was 1.872145, and the Average True Range was 2.054136. The SPY was 757.83, which is above the SMA200 of 711.49165. These are mixed signals and not a catalyst edge. An illustrative example with an entry at 27, a stop at 25.127855, and a target at 32.616435 has a gross 3R but a net 2.680557R at 5 basis points fees plus 25 basis points lip per side. These levels are not live orders, and the source is the adjusted close, not the current quote. One share at 27 fits a 49.14 fund, but a full single-share loss is 3.172775% of an 850.99 account, and the whole fund loss is 5.7745%. The observed worst daily gap was -21.4830%, which is not the maximum risk. The twenty-day average dollar volume is 35.515M, which does not guarantee a limit fill. The conclusion is no entry, and the levels were not adopted because the target cannot be justified from the need for a 349.01 profit. The source of this information is https://runway.adityaperswal.workers.dev/data/stock-bars.
A news scan found the text describing the performance of a trading model. The model is based on three premises. First, the momentum and distance from the Simple Moving Average, or SMA, of 20 days correlate at 0.927172 in 15 training events, but two transforms are not independent evidence. Second, a statistical analysis of the training data, using a method called Ordinary Least Squares, or OLS, shows that the return on momentum, Moving Average, or MA, distance, and other factors has a coefficient of determination, or R2, of 0.225275, with coefficients per standard deviation of 0.757688, -2.177770, -1.470071, and -0.928910, and an intercept of -0.480636. The Root Mean Square Error, or RMSE, of the holdout set is 3.184008 percentage points, versus 4.400283 percentage points for a constant model, but the improved prediction of losses does not create positive capital expectancy. Third, the model was tested on a subset of stocks, with results of -2.476747% on the training set and -12.291584% on the test set for one subset, and -3.778566% on the training set and -7.479860% on the test set for another subset. After a calendar event in 2024, the training set results were +8.058693%, but the holdout set results were -26.578284%. The conclusion is that no filter was adopted, and the full subset matrix saved observations, but was not selected after testing. The source of the information is https://runway.adityaperswal.workers.dev/data/stock-bars.
A news scan found the text describing the analysis of the CNXC91106 event. The analysis is based on two premises. First, primary scheduling announcements for 22 historical events were recovered before measuring event returns, from 2021 to 2025, and June 2026, excluding January 2021 because it was not recovered, with the earliest study being March 2021, but not all listing history is available. The analysis also excludes 6 events whose dates were not announced before entry, including same-day unknown hour, referred to as T10. Second, a frozen T5, which is a type of statistical model, with a 1 ADR stop, meaning a 1 average daily range stop, and a 3 ADR target, meaning a 3 average daily range target, of 30 basis points, or 0.3 percent, per side, showed a mean train return of -0.480636 percent and a holdout return of -4.292546 percent. A circular block, which is a method of resampling data, with 2 bootstrap, meaning two iterations of resampling, and 20000 seed, meaning a random number generator seed, of 91106, showed a mean 95 percent train return of -2.215259 percent to 1.293171 percent and a holdout return of -5.773292 percent to -3.038524 percent. The analysis is conditional on 7 selected company events and notes that resampling cannot discover unseen tails, meaning extreme events. The full calendar T5 time only holdout showed a return of -11.1555 percent versus a barrier return of -26.5783 percent, which shows that a tighter stop policy hurts this sample and does not justify removing required stops. The conclusion is to reject the primary rule. The validity of the analysis is limited to calendar vintages, selection, early news, and regimes, and the soundness is limited because the training and holdout datasets failed to propose an entry gate. This is a chronological holdout for a new rule, not a pristine fund-wide untouched dataset. The analysis also notes that September 2023 overlaps with Web help and excluding it, the train still returns -4.86638 percent N14. The primary source URLs are observations 24553 to 24575, and the data can be found at https://runway.adityaperswal.workers.dev/data/stock-bars.
A trading strategy called CNXC91106 was tested. The strategy had three main rules: first, it looked at when companies announced important information, and it only considered announcements made before a certain date. It also excluded announcements made on the same day as the trading session, if the exact time of the announcement was not known. Second, the strategy used historical data from 2021 to 2024 to train, and from June 2025 to September 2026 to test. It used a stop-loss at 1 average daily range below the entry price and a target at 3 average daily ranges above the entry price. The strategy only considered long trades, and it included fees and slippage of 5 basis points and 45 basis points respectively. The results showed that all variants of the strategy had negative returns during the training period, and even the best performing variant during the test period could not make up for the losses. The data used for the strategy came from individual company websites and a website called https://runway.adityaperswal.workers.dev/data/stock-bars. The strategy had some limitations, including only looking at one company, using historical data, and assuming certain costs. A news scan found the headline was not available, this is a raw script output.
A trading strategy called CNXC91106 was tested. The strategy had two main rules: first, it looked at the dates when companies announce their financial results, and it only traded between 10 days before and 1 day after these announcements, excluding the actual day of the announcement if it was before or after the market closed. Second, it used past data from 2021 to 2024 to train the strategy and data from June 2025 to September 10, 2026, to test it. The strategy was to buy and sell based on certain price movements, with a stop-loss at 1 average daily range below the entry price and a target at 3 average daily ranges above the entry price. The strategy also considered fees and slippage, which are costs associated with buying and selling, and it reinvested any profits. The results showed that the strategy did not work well, with all versions of the strategy resulting in negative returns. This was true even when looking at a shorter period of time. The data used for this test came from individual company websites and a website called https://runway.adityaperswal.workers.dev/data/stock-bars. There were some limitations to the test, including that it only looked at one company, it was based on past data, and it did not account for all possible costs and news events.
A trading strategy called CNXC91106 was tested. The strategy had three main rules. First, it only traded between 10 days before and 1 day before certain events, and it did not trade on the day of the event if it was announced after the market closed or before it opened. Second, the strategy used historical data from 2021 to 2024 to decide when to buy and sell, and it tested its performance using data from June 2025 to September 2026. It stopped trading if it lost a certain amount of money, and it set a target to make three times that amount. If the market opened lower than expected, it stopped trading immediately. The strategy only traded in one direction, which means it only bought and never sold short. It also accounted for the costs of trading, such as fees and the difference between the expected price and the actual price. The strategy was tested in different ways, but none of them made a profit. The data used for the test came from the internet, specifically from the URLs of the companies and a website called https://runway.adityaperswal.workers.dev/data/stock-bars. The test had some limitations, such as only using data from one company and not accounting for all possible costs and risks.
A trading strategy called CNXC91106 was tested. The strategy had two main rules: first, it used certain dates from the issuer, entering trades 10 days before and exiting the day before the event, excluding events that happened after the market closed or before it opened. It also excluded announcements made on the same day as the trade entry. Second, it used past data from 2021 to 2024 to train the strategy and data from June 2025 to September 2026 to test it. The strategy used a stop-loss at 1 average daily range below the entry price and a target at 3 average daily ranges above the entry price. If the trade opened with a gap, it would stop the trade. The strategy only considered long trades, with fees of 5 basis points and slippage of 25 basis points on each side. The returns were compounded and reconciled manually. A news scan found the headline was not needed, instead the strategy used synthetic data for bookkeeping. The strategy was tested with different variants, but all of them had negative returns, so it was not adopted. The data used for the strategy came from individual company URLs and a website. The test had some limitations, including only using data from one company, looking back at past events, and assuming certain costs.
A trading fund manager made notes about a trading strategy called CNXC91106. The strategy has two main rules: first, the manager looks at announcements from companies, but only if they happen before a certain date, and second, the manager uses a special adjustment called SIP to look at daily stock prices for two specific stocks, 1451CNXC and 1470SPY, from 2021 to 2026. The manager uses a computer program called vectorbt to make trades, only buying stocks and paying a fee of 5 basis points and a slippage of 45 basis points for each trade. The manager looks at the returns on investment and the number of trades made, and also considers the drawdown, which is the difference between the highest and lowest values of the investment. The manager uses a special measure called mean excess pp to compare the returns to a benchmark, and tests different variations of the strategy. The manager concludes that the strategy is not working because all the variations have negative returns, and even the positive results in the test period cannot make up for the losses. The manager got the data from individual company websites and a website called https://runway.adityaperswal.workers.dev/data/stock-bars. The manager notes that the study has some limitations, including only looking at one company, using old data, and not considering some costs and news events.
A news scan found the text describing the CNXC91106 T-10 time exit train with a 50bp/side condition. The premises of this trading strategy are as follows: first, the strategy uses Freeze2229, which involves entering trades at the open on T-10 and exiting at the close on T-1, excluding event sessions for AMC and BMO, and only considering announcements that predate the entry date. Second, the strategy uses a SIP adjustment with all 1451CNXC and 1470SPY daily bars through 2026-09-10, training from 2021 to 2024, and holding out from June 2025 to 2026. The strategy has a fixed calendar time exit with no stop and is for research control only, not eligible for live policy. Third, the strategy uses vectorbt with long-only orders, fees of 5bp, and slippage of 45bp on each side. The returns and trade counts are reconciled manually, and the initial cash is included in the mark-to-market drawdown. The strategy also uses synthetic two rows per day for bookkeeping, not intraday data, and a fractional capital reinvestment proxy. The mean excess return is the difference against the full T-10/T-1 SPY interval, not exposure-matched alpha. The conclusion is that there will be no adoption of this strategy because all training variants had a negative compounded return, and the positive holdout in T3 cannot rescue the failed train. The sources of the issuer URLs are individually listed in observations 24553-24575, and additional data can be found at https://runway.adityaperswal.workers.dev/data/stock-bars. The limitations of this strategy include the selection of a single company, retrospective calendar recovery, overlap with 2023 Webhelp, 22 events, possible early news, assumed costs, and a 15-minute and +2R policy that is separate.
A trading fund manager made decisions based on certain premises. The first premise is about a trading strategy called Freeze2229, which involves entering trades 10 days before a certain event and exiting on the day before the event, excluding events that happen after the market closes or before it opens. The manager also considered a system called SIP adjustment, which looked at daily stock prices for CNXC and SPY from 2021 to 2026, with a specific time period set aside for testing. The manager used a system called vectorbt to make trades, only going long, with fees of 5 basis points and slippage of 25 basis points on each side. The manager manually checked the returns and trade counts, and included the initial cash in the calculations. The manager also used a system to track the performance of the trades, comparing it to the performance of the SPY stock over a certain time period. The manager tested different variations of the strategy, but none of them had a positive return, so the manager decided not to adopt the strategy. The manager got the data from individual company websites and a website called https://runway.adityaperswal.workers.dev/data/stock-bars. The manager noted some limitations of the study, including that it only looked at one company, used past data, and did not account for all possible costs and news events.
A news scan found the following trading fund notes. The fund manager made three premises. First, the manager considered Freeze2229, which involves the issuer's advance dates, entering at T-10 open and exiting at T-1 close, excluding event sessions for AMC and BMO. Announcements must be made before the entry date, and same-day unknown release times are excluded. Second, the manager made a SIP adjustment using all 1451CNXC and 1470SPY daily bars through 2026-09-10, training from 2021 to 2024, and holding out from June 2025 to 2026. The manager used a fixed calendar time exit with no stop, for research control only, and not for live policy. A stop is a price level that, when reached, automatically closes a trade. Third, the manager used vectorbt, a backtesting framework, with long-only orders, 5 basis points fees, and 25 basis points slippage on each side. Basis points are units of measurement for interest rates and fees. Slippage refers to the difference between expected and actual trade execution prices. The manager reconciled compounded returns and trade counts manually and included initial cash in mark-to-market drawdown. Mark-to-market is a method of valuing assets at current market prices. The manager used synthetic two rows per day for bookkeeping, not intraday data, and fractional capital reinvestment proxy. The mean excess return is the difference against the full T-10/T-1 SPY interval, not exposure-matched alpha. Alpha refers to the excess return of an investment relative to the market. The manager froze Sweep3/5/10 and fixed primary T5, reporting all variants without picking a test winner. The conclusion was no adoption because all training variants had negative compounded returns, and T3 positive holdout could not rescue the failed train. The sources are issuer URLs individually in observations 24553-24575 and https://runway.adityaperswal.workers.dev/data/stock-bars. The limitations include a selected company, retrospective calendar recovery, 2023 Webhelp overlap, 22 events, possible early news, assumed costs, and 15-minute and +2R policy separate.
A news scan found the following trading notes. The trading strategy is based on two premises. First, the strategy involves entering a trade five days before a specific event and exiting the trade one day before the event, excluding events that happen after the market closes or before it opens. The strategy also excludes announcements that are made on the same day as the trade entry, if the release time is unknown. Second, the strategy uses historical data from 2021 to 2024 to train a model, and then tests the model on data from June 2025 to September 10, 2026. The strategy involves setting a stop loss at 1 average daily range below the entry price and a target at 3 average daily ranges above the entry price. If the market opens with a large gap, the stop loss is triggered. The strategy only considers long trades, and it includes fees of 5 basis points and slippage of 45 basis points on each side of the trade. The returns are compounded and reconciled manually. The strategy also includes a bookkeeping adjustment to account for fractional capital reinvestment. The results show that all training variants had negative compounded returns, and even the positive holdout results were not enough to rescue the failed training. The sources of the data are listed individually, and the data can be found at https://runway.adityaperswal.workers.dev/data/stock-bars. The strategy has several limitations, including the selection of a specific company, the use of retrospective calendar recovery, and the assumption of certain costs. A headline about the trade results mentioned a 50 basis point per side stop and a 3 average daily range target holdout.
A news scan found the following trading notes. The premises of the trade are as follows: the trade is based on an automated system called Freeze2229, which uses issuer advance dates to enter a trade five days before an event and exit the day before the event, excluding events that happen at the start or end of the trading day. The system also excludes announcements that are made on the same day as the trade entry, if the exact release time is unknown. The system was trained on historical data from 2021 to 2024, using daily price bars for the ticker symbols 1451CNXC and 1470SPY up to September 10, 2026. The system uses a stop-loss at 1 average daily range below the entry price and a target at 3 average daily ranges above the entry price. If the trade opens with a gap, the stop-loss is triggered. The system only considers long trades, with fees of 5 basis points and slippage of 45 basis points on each side. The returns and trade counts are reconciled manually, and the initial cash is included in the mark-to-market drawdown. The system uses a mean excess return metric, which is the difference between the return of the trade and the return of the SPY ticker over the same period. The results of the trade are negative, with all training variants showing a negative compounded return. The system was tested on a holdout period from June 2025 to June 2026, but the results were not positive enough to rescue the failed training period. The sources of the data are listed in observations 24553 to 24575, and are also available at https://runway.adityaperswal.workers.dev/data/stock-bars. The limitations of the system include the selection of a single company, retrospective calendar recovery, overlap with web help in 2023, 22 events, possible early news, assumed costs, and a 15-minute and 2R policy that is separate from the main system.
A trading strategy called CNXC91106 was tested. The strategy had two main rules: first, it would start trading five days before a certain event and stop trading one day before the event, excluding events that happen at the start or end of the trading day. Second, it would use past data from 2021 to 2024 to train the strategy and then test it from June 2025 to September 10, 2026. The strategy would buy a stock and set a stop-loss at the price minus one average daily range and a target at the price plus three average daily ranges. If the stock opened at a bad price, the strategy would stop the trade immediately. The strategy was tested with fees of 5 basis points and slippage of 25 basis points on each trade. The results showed that the strategy did not work well, with all versions of the strategy having negative returns during the training period. Even the version that worked well during the test period could not make up for the losses during training. The data used for the test came from the internet and a website called runway.adityaperswal.workers.dev. There were some limitations to the test, including only using data from one company and not accounting for all possible costs and news events.
A trading strategy called CNXC91106 was tested. The strategy had two main rules: first, it looked at the dates when companies announced their financial results, and it started trading five days before these announcements and stopped one day before. It avoided trading on the days of the announcements if they happened before or after the market closed. Second, it used past data from 2021 to 2024 to train the strategy and data from 2025 to June 2026 to test it. The strategy was tested on a specific stock, CNXC, and compared to the performance of the SPY stock index. The goal was to make money by buying the stock, with a stop-loss at 1 average daily range below the entry price and a target at 3 average daily ranges above the entry price. A stop-loss is a price at which to automatically sell a stock to limit losses, and a target is a price at which to automatically sell a stock to secure profits. The average daily range is the difference between the highest and lowest prices of a stock in a day. The strategy also accounted for fees and slippage, which is the difference between the expected price of a trade and the actual price. The results showed that the strategy did not work well, with all versions of the strategy resulting in negative returns. This means that the strategy lost money. The data used for the test came from individual company websites and a website called https://runway.adityaperswal.workers.dev/data/stock-bars. The test had some limitations, including only looking at one company, using past data, and not accounting for all possible costs and news events.
This is a report about a trading strategy called CNXC91106. The strategy is based on two main ideas: first, to buy and sell stocks based on certain dates, and second, to adjust the strategy using historical data from two stocks, 1451CNXC and 1470SPY, from 2021 to 2026. The strategy was tested using a computer program called vectorbt, which simulated buying and selling with certain fees and slippage, meaning the difference between expected and actual prices. The results showed that the strategy did not work well, with negative returns in all training variants, and even a positive result in one test period could not make up for the overall failure. The data used for this strategy came from individual company websites and a website called https://runway.adityaperswal.workers.dev/data/stock-bars. There are some limitations to this strategy, including that it only looked at one company, used past data, and did not account for all possible costs and news events.