A diagnostic test was run on the stock symbol MU using a momentum strategy, which is not a plan for actual trading. The test used a computer program to look back at 20 days of data and simulate 1095 days of trading, with a 5% stop loss and a 15% target profit. The results showed a 572.77% return, with 44.87% of trades being winners and an average trade profit of 2.77%. The test also showed a maximum loss of 43.83% at some point. This test did not take into account any fees or costs that might occur in real trading, and it is not a recommendation to buy or sell the stock. The stock price of MU is currently too high for the fund to buy a whole share, which would cost more than $49.14. The results of this test should not be used to predict future trading outcomes, and a separate study on the stock symbol CAG showed different results. The data used for this test came from the website https://runway.adityaperswal.workers.dev/data/stock-bars.
A diagnostic test was run on the momentum strategy for stocks CAG and MU. The test used a 20-day lookback period, 1095 days of data, a 5% stop loss, and a 15% target profit. The results for CAG showed a 6.22% return, compared to a -48.87% return for a buy-and-hold approach. The test also showed a 24.04% maximum drawdown, a 42.86% win rate, and an average trade return of 0.27%. This test is not a strategy for actual trading and does not account for fees or slippage. A separate study on CAG using vectorbt found different results and rejected entry. The test also showed that the trend profit for MU does not necessarily mean there is an edge in earnings run-up, and buying one whole share of MU would exceed the fund's capacity of $49.14. The data for this test came from https://runway.adityaperswal.workers.dev/data/stock-bars. The results for MU should not be used to predict future performance for a small fund.
A study of Conagra Brands earnings was done from 2021 to 2026. The study used actual earnings data from the company's website and other sources. The data was used to test a trading strategy with a 10-day window and a 15 basis point slip. The strategy had a return of -6.27% and a drawdown of 9.47% during the test period from 2025 to 2026. The strategy had a win rate of 33.33% and an average win of 3.14%. The study also tested the strategy with different parameters, but none of them performed better. The study did not validate the execution of the trades and did not audit the pre-announcement period. The results are based on historical data and not on future projections. A news scan found the website https://www.conagrabrands.com/investor-relations/financial-news/earnings-announcements as a source of earnings announcements.
A study on ConAgra Brands' earnings was conducted using data from 2021 to 2026. The study used a fixed strategy to predict earnings, with a 15 basis point slip. The training data consisted of 17 events from 2021 to 2024, and the held-out data consisted of 6 events from 2025 to 2026. The sources of the data included the company's investor relations website and a stock bars dataset. The study used a rule-based approach to enter trades, with a stop-loss set at 1 average true range, or ATR, which is a measure of volatility. The target was set at 3 ATRs. The study also used a daily open, high, low, and close, or OHLC, proxy to determine entry and exit points. The results showed a return of 1.3255, a drawdown of 10.0409, and a win rate of 41.18. The study used a vectorized portfolio with open and close marks, and 1 basis point fees and 15 basis points of slippage on each side. The return agreed with manual compounding to less than 3e-15. The study found no qualified entry. The historical dates used were actual release dates, but a full pre-announcement audit was not done. This study is a new reserved historical holdout for ConAgra Brands, not prospective forward data.
A study of ConAgra earnings was done from 2021 to 2026. The study used actual earnings data from the ConAgra website and other sources. The data was used to train a trading model from 2021 to 2024 and test it from 2025 to 2026. The model used a simple stop-loss strategy, which is a rule to sell a stock when it falls by a certain amount, in this case, one Average True Range, or ATR, which is a measure of how much a stock's price moves. The model also used a target price, which is three ATRs away from the entry price. The results showed a return of -5.14% and a drawdown of 8.47% over six trades. The model had a win rate of 33.33% and an average win of 3.35%. The study found no qualified entry, meaning the model did not find a good time to buy the stock. The study used historical data and did not validate the results with actual executions. A news scan found the source of the data to be from the ConAgra website and other online sources.
A study on Conagra Brands (CAG) earnings was conducted using actual data from 2021 to 2026. The study removed a duplicate entry from January 4 and used fixed returns before 2025. The data was sourced from the Conagra Brands website and other online sources, including https://www.conagrabrands.com/investor-relations/financial-news/earnings-announcements and https://runway.adityaperswal.workers.dev/data/stock-bars. The study used a rule to enter trades at the open price 10 days before an event, with a stop loss set at 1 times the average true range (ATR) and a target set at 3 times the ATR. The study also used a daily high-low-close-open (OHLC) proxy to adjust for gaps and stops. A vectorized backtest was run with a portfolio that included open and close marks, 1 basis point fees, and 5 basis points slippage on each side. The results showed a return of 4.83%, a drawdown of 8.95%, and a win rate of 41.18% over 17 samples. The study also found that the half Kelly fraction was 3.58 and the manual VBT error was 8.60e-16. The study used a T5 model and found that the T3 and T10 models were sensitive to the data, but no qualified entry was found. The historical dates used in the study were actual release dates, but a full pre-announcement audit was not done. This study is a new reserved historical holdout for CAG and not prospective forward data.
A study of ConAgra Brands earnings was done from 2021 to 2026. The study used actual earnings data from the company's website and other sources. The data was used to train and test a trading strategy. The strategy used a 3-day average true range, or ATR, to set stop-loss and target prices. The strategy was tested on data from 2025 and 2026. The results showed a return of 2.32% and a maximum drawdown of 5.65%. The strategy had a win rate of 50% and an average win of 2.63%. The study also found that the strategy had a high half-Kelly fraction, which is a measure of risk. The study did not validate the execution of the trades and did not audit the pre-announcement data. The results are based on historical data and not prospective forward data. A news scan found the source of the data to be from the company's investor relations website.
A study of ConAgra Brands earnings announcements from 2021 to 2026 found that training a model on data from 2021 to 2024 resulted in a return of -2.9116% with a drawdown of 5.862%. The model used a simple moving average and average true range, a measure of stock price volatility, to set stop-loss and target prices. The model was trained on 17 data points and tested on 6 held-out data points from 2025 to 2026. A 15 basis point slip in price was assumed for each trade, and a 1 basis point fee was charged. The results showed a win rate of 47.06% and an average win of 1.1768% and an average loss of 1.3521%. The model did not select a best holdout, and no qualified entry was found. The study used historical data and did not validate execution or audit pre-announcement data. A news scan found the source of the earnings data at https://www.conagrabrands.com/investor-relations/financial-news/earnings-announcements. The study also used data from https://runway.adityaperswal.workers.dev/data/stock-bars for explicit SIP adjustment. The half-Kelly fraction, a measure of optimal bet size, was 0. The study found that the two-outcome half-Kelly raw value can exceed 1 for variants, but this is not a leveraged authorization, and the qualified primary allocation is 0.
A study of ConAgra earnings was done using data from 2021 to 2026. The study used actual earnings data from the company's website and other sources. The data was used to train a trading model from 2021 to 2024 and test it from 2025 to 2026. The model used a simple rule to buy and sell stocks: it would buy on the third day before an earnings announcement and sell on the day before. The model also used a stop-loss order to limit losses. The results of the study showed a return of 3.55% and a maximum loss of 5.27%. The model had a win rate of 50% and an average win of 2.83%. The study also found that the model had a high sensitivity to certain parameters, but no qualified entry was found. The study used historical data and did not validate the results with actual executions. The data used was from actual earnings releases and was not prospective or forward-looking.
A study of ConAgra Brands earnings was done from 2021 to 2026. The study used actual earnings data from the company's website and other sources. The data was used to train a trading model from 2021 to 2024 and test it from 2025 to 2026. The model used a simple rule to buy and sell stocks: it bought on the third day before the earnings announcement and sold on the day before. The model also used stop-loss orders to limit losses. The results showed a return of 0.4462, a drawdown of 4.3088, and a win rate of 52.94%. The model was tested with different parameters, but no qualified entry was found. The study used historical data and did not validate the results with actual executions. A news scan found the website https://www.conagrabrands.com/investor-relations/financial-news/earnings-announcements as a source of earnings announcements.
A study of ConAgra earnings was done from 2021 to 2026. The study used actual earnings data from 23 issuers, which was first reported at 07:30 ET on certain dates. The data from 2021 to 2024 was used for training, and the data from 2025 to 2026 was held out for testing. The sources of the data were the ConAgra website and a stock bars website. The study used a rule to enter trades at the open price five days before an event, with a stop loss set at one average true range, or ATR, which is a measure of price volatility. The target price was set at three ATRs. The study also used a daily high-low-close-open, or OHLC, proxy to check for gap stops and targets at the open price. A vectorized backtest was done on a bitcoin cash portfolio with 1 basis point fees and 15 basis points slippage on each side. The results showed a return of -5.0398%, a drawdown of 7.3337%, and a win rate of 66.67%. The study also found that the T5 model had negative results in both training and holdout periods, while the T3 model had positive results in the base period but negative results in the stress training period. The T10 model had negative results in the holdout period. No qualified entry was found. The historical dates used in the study were actual release dates, but a full pre-announcement audit was not done. This study is a new reserved historical holdout for ConAgra, not prospective forward data.
A study on ConAgra Brands earnings runup was done from 2021 to 2026. The study used actual earnings data from the company's website and other sources. The data was used to train a model from 2021 to 2024 and test it from 2025 to 2026. The model used a simple rule to buy and sell stocks: it would buy at the open price five days before an earnings announcement and sell at the close price the day before. The model also used a stop-loss order to limit losses. The results of the study showed a return of -7.44% and a drawdown of 9.49%. The model had a win rate of 47.06% and an average win of 1.28%. The study also tested the model's sensitivity to different parameters and found that it was not sensitive to changes in the parameters. The study did not validate the model's performance on out-of-sample data and did not audit the pre-announcement period. The model's results are based on historical data and are not intended to be used for prospective investment decisions.
A study on ConAgra Brands' earnings was done from 2021 to 2026. The study used the company's actual earnings announcements from their website and other sources. It looked at what happened to the stock price before and after these announcements. The study used a rule to decide when to buy or sell the stock. This rule said to buy at the opening price five days before the announcement, and to sell if the price went down by a certain amount or up by three times that amount. The study also looked at how much money was made or lost, including small fees and costs. The results showed a return of -3.8935% and a biggest loss of 6.3087%. The study was done on a part of the data that was not used for training, from 2025 to 2026. This was a test of the rule, and it did not find any good times to buy the stock. The study only looked at what happened in the past and did not try to predict what will happen in the future.
A study of ConAgra Brands earnings was conducted using data from 2021 to 2026. The study used a fixed set of earnings data, with the first news dates at 07:30 ET, and removed a duplicate entry from January 4. The data was split into training and holdout sets, with 17 entries from 2021 to 2024 and 6 entries from 2025 to 2026. The sources of the data included the ConAgra Brands website and a data retrieval system. A stop-loss strategy was used, with a 1 ATR stop and a 3 ATR target, and the entry point was set at T-5 open. The daily high, low, open, and close prices were used as a proxy, with gap stops and targets set at the open. A vectorized portfolio was used, with 1 basis point fees and 5 basis points slippage on each side. The results showed a return of -4.24%, a drawdown of 7.77%, and a win rate of 52.94%. The study also found that the T5 model had negative results in both training and holdout sets, while the T3 model had positive results in the base case but negative results in the stress test. No qualified entry was found, and the historical dates used were actual release dates, with no full pre-announcement audit done.
A diagnostic test was run using a command line interface to evaluate a momentum-based trading strategy on the stock SCHL. The test, which used 752 bars of data and resulted in 50 trades, showed a return of -20.343012% and a maximum drawdown of 46.486672%. The test also showed that the strategy was only successful 30% of the time, with a mean trade return of -0.284097%. For comparison, a buy and hold approach would have resulted in a return of -16.387760%. The data used for the test came from https://runway.adityaperswal.workers.dev/data/stock-bars. This test was a basic diagnostic and did not take into account factors such as fees and slippage, and the results cannot be used to authorize the use of capital. A separate study, known as event study 2768, used more detailed data and a longer time period, and also showed negative results for the momentum-based strategy. The test was run on September 24 and the results confirm that the momentum-based strategy is not valid. The conclusion is that there is no basis for using a momentum-based entry strategy, and no parameters were found that could improve the results.
A trading fund manager's notes were frozen before new returns were added. The notes cover the period from T-5 open to T-1 close, with a stop loss set at 1 times the prior 20-day average daily range and a target set at 3 times the average daily range. The manager used a static daily proxy and verified 22 issuer release dates from the Scholastic investor website. The manager trained the model from 2021 to 2024 and used data from 2025 to July 2026 as a known holdout. The manager used adjusted data from the IEX exchange, which is a stock exchange that provides real-time data, and a Python custom script to fill orders with targets capped. The manager also used a vectorbt long-only strategy, which is a type of trading strategy that only buys stocks, with fees of 5 basis points and a slip of 10 basis points on each side. The initial cash was set at 100. The manager reconciled the count and compounded returns exactly. The training return was -2.64033%, with a drawdown of 12.96266% and a win rate of 37.5%. The mean return was -0.107976% and the W ratio was 4.11688%. The L ratio was 2.64289% and there were 5 targets. In the known holdout period, the return was +2.15046%, with a drawdown of 7.22507% and a win rate of 66.667%. The mean return was +0.407757% and the W ratio was 2.16255%. The L ratio was 3.10184% and there were zero targets. A bootstrap analysis with 20,000 iterations and a 95% confidence interval showed a mean return range of -2.03654% to 3.12124%. A stress test with a 25 basis point slip showed a training return of -7.20323% and a holdout return of +0.328197%. The manager concluded that the gross 3R geometry was less than the net 3R and that empirical wins were time exits, not demonstrated tail capture. The Half Kelly capital formula, which is a formula used to determine the optimal amount of capital to allocate to a trading strategy, yielded 0 in training and was mechanically capped at 1 in the selected known holdout, but was found to be unstable and not adopted. The manager noted that prior research had seen the same holdout results and that this was a changed exit robustness experiment, not a fresh independent out-of-sample test. The manager also noted that historical date announcement availability and early news and corporate action execution remained gaps. The conclusion was no entry and to preserve the September 17 paper window, not to enter on Monday earlier. The scripts and research are saved in the file test_schl.py and the trade outcomes are saved in the file manifest observations and trade outcomes 25287.
A review of the PRE91108 frozen backtest audit was conducted, reusing existing results without running new independent tests. The review examined the original data, which consisted of 244,382,848 entries, without re-running the tests or selecting a new pooled sample or holdout. The results for HITI showed a fixed T-3/T-1 vectorbt with training from 2022 to 2023, resulting in a return of 31.7294% for 8 periods, and a holdout from 2024 to June 26 with a return of 3.75545% for 11 periods, a drawdown of 8.65185%, and a win rate of 63.6364%. The stress test result was 0.38739%, with a mean confidence interval of -0.73259% to 1.53296%. For HAIN, the results showed a T-10/T-1 with training for 13 periods, resulting in a return of 39.3858%, and a holdout for 6 periods with a return of -54.89695%, a drawdown of 56.6170%, and a win rate of 16.6667%. For PLAY, the results showed a T-4/ATR exit with training for 16 periods, resulting in a return of -13.155%, and a holdout for 6 periods with a return of 6.87897%, a drawdown of 12.73711%, and a win rate of 66.6667%, with a mean confidence interval of -4.485% to 7.251%. The prior CLI controls, which consisted of 280, 393, and 848 entries, were also reviewed, and it was noted that generic momentum is not a valid event validation. All studies included assumed fees and slippage, as well as chronological splits, and the reviewed holdouts are now known, rather than being untouched forward evidence. The conclusion of the review was to preserve the rejected or inconclusive rules. All tested entry windows passed before the final pre-event session, and a new intraday purchase would require a new independent test. The sources used for the review included https://runway.adityaperswal.workers.dev/data/stock-bars, https://ir.hain.com/financial-information/quarterly-results, https://hightideinc.com/high-tide-to-announce-third-fiscal-quarter-2026-financial-results/, and https://daveandbusters.gcs-web.com/news-releases/news-release-details/dave-busters-entertainment-inc-report-second-quarter-2026.
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.
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.