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Introducing

Case Study: Algorithmic Trading With Go

Jun 30, 2023

In this case study, we are excited to share an insider's perspective and look under the hood of how a Polygon.io customer, who built an automated retail trading bot that is capable of monitoring the entire stock market in real-time. We now pass the baton where they will narrate his own captivating journey.

Hi, the early versions of my bot could have easily won an award for 'The Fastest Money-Losing Machine Ever'. But, after lots of trial and error, and iterative improvements, it evolved into a breakeven machine, and sometimes even turns a profit. It remains a work in progress, but the trajectory is promising. Keep in mind that my 3+ year journey with this led me to become so familiar with the platform, Polygon.io, that I joined their team to work on this type of stuff full time. I wanted to share my story as a blueprint for those who might want to attempt something similar.

No tale of algorithmic trading would be complete without the obligatory screenshot of an impressive multi-monitor setup. Behold my data-driven empire, brought to life on three vertical 4K screens.

So, buckle up and join me as we navigate the exciting crossroads of finance, programming, and data analytics.

The Challenge

Have you ever had one of those ideas that you cannot shake? Sort of like a song that gets stuck in your head? Well, I've had that but for this stock trend following idea. The goal here is to automate the execution and management of around 500 short lived trades across the entire stock market and then take small profits off each trade.

My attempts at doing this manually underscored why this needed to be automated. First, there was the issue of human bandwidth, the risk associated with pouring all my investment into a single stock was astronomical, yet monitoring tens of short-lived bets was a task beyond my ability. Second, this strategy only worked with highly liquid stocks that allowed swift in-and-out trading, and even then, I did not want to acquire too many shares to make for faster fills at prices I liked.

Position management proved to be a massive issue for me. How do you track multiple bets, say 25+ at a time, all while timing the entries and exits was very complex. There was also the question of scalability. The strategy I am using only works with smaller amounts of money, so as my bank roll increased so did the amount of bets needed. Finally, the costs associated with trading - price spread, commissions, price slippage, API fees, and taxes - all need to be calculated quickly to see if this trade even makes sense. That’s how I started to explore automating it.

The Solution

The journey to develop an automated trading tool capable of monitoring over 5500+ stocks on the NYSE and NASDAQ in real-time and making quick trading decisions has taken a few years. It has been a constant cycle of trial, error, and lots of tweaking, a far cry from the few months I initially anticipated. The application is running under Linux on a high-powered gaming system with 16 cores, 128GB of RAM, and 8TB of NVMe storage via a 1Gbps internet connection.

Here is a breakdown of the three fundamental components:

• Data Provider: Initially, I had no idea what to even search for here? Candlestick data? What was tick data? This was a massive learning curve and finding a reliable data feed had so many obstacles. Many providers had unconventional APIs, insisted on using their preferred programming languages, or required installation of obscure libraries. My lack of knowledge combined with these other factors created a highly cumbersome process. However, I hit the jackpot with Polygon.io . They have both historical and real-time data coverage of the entire market, developer friendly and intuitive APIs, and amazing documentation and libraries . I cannot express how much they stood out compared to everyone else. They also had a simple pricing model that gives you access to all market data without artificial limits like other providers.
• The Application : This Go app is basically the heart of the solution. It ingests and interprets the data feeds, makes calculated trading decisions, and translates these decisions into buy and sell orders. These orders are then sent to the broker API for execution. We will dive deeper into the specifics of the application in the following section.
• The Broker : When it came to finding a broker, my regular brokerage account did not provide an API. While I would like to say that I conducted extensive search, in reality, a quick Google search revealed that Interactive Brokers was the go-to choice for many. After checking their APIs and finding them to be quite straightforward, I decided to go with them.

Why Go, you might be asking yourself? A quick glance at any hedge fund job posting reveals that the industry leans heavily towards C++ and Python. But this project was for me. Having used Go for many years, I found it to be an excellent fit for processing a stream of data and interacting with another API. So, it just seemed so obvious.

Let's break down the main functions of the Go application:

• Data Ingestion Loop : This function is responsible for continuously gathering real-time data from over 5500+ stocks via polygon.io. The quality of data ingested at this step directly impacts every subsequent operation, so you want to put a lot of thought into this step and make it as simple as possible. Initially, I tried storing the data in a database but quickly realized that it couldn't handle the high volume of events (upwards of 60k+ events per second at market open and close). Instead, I opted to store everything in memory and have not looked back.
• Build Our View Of The Stock Market : The application uses the ingested data to create a real-time, in-memory view of the entire market, tracking every stock (price, spread, trading activity, etc). This custom view allows the system to spot and trade on opportunities way before they hit mainstream news. This also gives you access to pre-market, regular market, and after hours trading activity.
• BUY Signal Loop : Upon spotting an opportunity, this function sends the broker an API request to place a buy order. But it's not just a simple trigger. The function carries out pre-calculations such as estimating profit based on price spread, determining the number of shares needed, and factors in commissions and taxes. It also incorporates logic for situations where the price moves, or where we only get a partial fill, or maybe need to cancel the order if it doesn’t make sense any more.
• Position Tracking System : Once trades are executed and we own stock, it's vital to monitor the positions. I consider this system like a vigilant shepherd overseeing its flock of positions. Essentially, it is a loop that continuously compares the position table with current prices, tracking gains and losses in real-time. There is also a GUI here where you can go and inspect the trade to see why it was triggered, what the current state is, and when it would sell. This has been instrumental in helping me refine the logic for the BUY and SELL loops.
• SELL Signal Loop : As crucial as knowing when to buy is knowing when to sell. This function calls the broker API to execute a sell order when the system detects a good exit (nice profit or we are losing too much money). Similar to the BUY function, this one incorporates complex logic too. It manages scenarios like updating the price for fast-moving stocks and handling partial fills.

At a glance, the core components might seem straightforward, but as time goes by and you encounter various corner cases, you will inevitably add more logic to these areas.

Screenshots

Having a built- in web interface has been immensely helpful in understanding the inner workings of a complex project, in that you can explore all of the data structures, visualize data, and dive deep into trades and see why they were triggered and their current status. To help bring this to life, I have included a series of screenshots. These not only offer a glimpse into the user interface of the trading system but also provide visual demonstrations of the system's key structural and functional elements.

This is an overview of all 5500+ tickers, complete with their prices, spread, and other relevant data.

This is a specific symbol page for Tesla (TSLA), showing tick bars and other pertinent information.

This interface displays the win/loss ratios and current open positions for active trades. You can see for this session we're down about -$900. This bot doesn't win all the time that's for sure.

Here you'll see a live trade in action, complete with all its associated metadata and an accompanying chart to visualize the trade's progress. The orange line is where we bought 24 shares.

Lastly, a glimpse into the console that logs real-time events such as order placements, both buying and selling.

I hope these screenshots have shed some light on the inner workings of this trading tool and helped illustrate the key functionalities more vividly. My aim was to bring the abstract concepts and processes to life, making it easier for you to understand and appreciate the complexities of the system.

Strategy Development and Backtesting

You might be expecting to read about specific strategies and perhaps their associated Sharpe ratios. However, you won't find that here. I think of the Go application, the strategies it employs, and backtesting as three distinct, yet equally crucial components. My primary focus has been on creating my own platform that allows me to test and run custom strategies in real-time, and also to backtest them. This aspect, the platform itself, seems to be often overlooked in most discussions. Many conversations revolve around strategies (mean reversion, trend following, linear regression, etc.), and backtesting, without fully addressing the practical mechanics or logistics of strategy implementation, particularly in the context of live, intraday trading.

In parallel to developing this platform, I've been leveraging the a wide range of historical trade and quote data provided by Polygon.io . This data is fundamental for exploring strategies using Python and backtesting them. Hence, the reason for the 8TB of NVMe storage on my machine. This approach enables me to download and structure the data, and then brainstorm and visualize potential trades. While it's often said that backtesting is akin to trying to drive forward while looking in the rearview mirror, it provides a valuable sanity check and offers insight into past performance. This helps me to make educated guesses, especially about spread, taxes, commissions, entry, and exit points, among other factors.

Ultimately, the insights gleaned from strategy development and backtesting are translated into logic that is incorporated into the 'BUY signal loop'. Algorithmic trading may sound sophisticated, but at its core, it's about identifying trends and patterns and trying to capture them programmatically.

Now that we've covered the overview, let's delve into some pseudocode to understand how it all comes together.

Code Examples

In the interest of giving you a more concrete understanding of how this system functions, I have included some high-level pseudo code and actual Go code samples below. These snippets provide a simplified depiction of the system's structure and the logical flow it follows. Please note that the Go code, while outlining the essential data structures and steps, is not functional and just example code (the real app is around 7k lines). It's merely a high-level depiction of how you might set up the primary data structures and routines in Go.

Here's what that sort of looks like in Go. This code doesn't work but gives you the high-level details of the major data structures.

The code presented here barely scratches the surface and is intended to providing a glimpse into the thought process and structure behind it. This includes defining key data structures, maintaining an in-memory index of symbols, handling real-time events, and integrating buy and sell trade logic. For a fully functional trading bot, you would need to incorporate much more sophisticated features but hopefully you get the idea.

Lessons Learned

Creating an automated trading tool has been nothing short of an adventure that crosses multiple domains, from finance and programming to data analytics. Through this project, I have come to learn and appreciate the complexity of the stock market and how hundreds of billions of dollars change hands each day. There are countless trading strategies out there and having your own platform to play around with them is incredibly cool. Once you have a system like this you cannot easily go back to a normal broker since you feel blind. I wanted to share of the key lessons out of all this:

• Understanding Abstractions : The stock market, and exchanges like NYSE are NASDAQ, are not monolithic entities but are actually large distributed systems built up from 19+ exchanges, each with its own quirks and features. Candlestick data is a massive abstraction built from raw trade or tick data, and understanding these abstractions at a deep level is essential. Then you have market trading times such as pre-market, regular market, or after-hours where different rules apply. Getting as close to the source data as possible and thoroughly understanding its origins and usage will significantly enhance your ability to leverage it.
• Order Management : It is not as simple as sending buy and sell orders. Factors such as pre-computed position sizing (the number of shares to buy and your total money percentage), the ability to quickly buy and sell shares, having the ability to manage 25+ active positions and act on them instantly, tax and commission calculations, slippage management, and order state monitoring all contribute significantly to successful trading.
• Edge Cases : Order execution, tracking, modifications, cancellations, partial orders, market halts and more, there are so many edge cases to consider and these cost you money if you miss something. It is absolutely essential to test these scenarios with simulated money, also known as paper trading, rather than real funds to minimize potential losses. I lost a lot of money when my system detected a trend in the pre-market and the stock jumped 40%, I bought at the absolute high, and then it quickly dropped, and my system did not adjust the sell order correctly. I basically lost 40% on that trade in minutes. You can make and lose money extremely fast in pre-market or after hours trading sessions since the normal market rules are different and you can see wild swings take place extremely quickly.
• Embrace Randomness : Many of us might be tempted to think that the key to a successful trading bot lies in discovering some secret, all-powerful strategy. While strategies are undeniably important, they are not everything. One of the most valuable lessons I learned was the utility of random buying to test the core functionalities of the system. For example, try to make 1000 trades per day across random stocks for a week and you'll learn so much about your system. By introducing random buy orders into the system, you can effectively test your buying and selling logic, manage partial fills, test cancel logic, and critically evaluate the overall position tracking system. Does your system keep track of them as expected? Can you delve into the trade details? Do the positions exit based on the established criteria? Is the logging working as intended? I incorporated random stock buying using my paper trading account into my testing process, which turned out to be an incredibly efficient way to verify multiple aspects of the system simultaneously. Not to mention, it made testing a lot more unpredictable and fun.
• Tick Bars vs Time Bars : When you look at a candlestick bar offered by your broker, it will have an open, high, low, close, number of trades, volume, etc, this covers a known time frame, for example 30 seconds. However, the issue here is that there are times where the market is moving extremely quickly and not all bars are created equally, some might have 100 trades while others have 1,000s of trades in the same time span. So, I am taking the raw tick and quote data and building my own bars based off a set number of trades. This not only provides much better resolutions during times of increased trade activity, like market open and close, but also enables me to add in things like price spread, and other custom metrics. This is where really understanding the data you are using comes in and you can build your own abstractions rather than using someone else's.
• Going In-Memory : In the early stages, I faced numerous challenges attempting to maintain state in a database because of the large spikes in activity around market open and close. Eventually, I decided to go entirely in-memory, utilizing a large map with mutex locking that virtually every component of my system interacts with. Although it was very challenging to construct and debug, this solution ultimately solved all my scalability issues. I configured the system to dump the struct that holds all data into a compressed gob file for storage, with a method to reload it in case I needed to restart the application. It can grow to be 40GB+ throughout the day and I needed to patch the Go build to support dumping gobs this large. This ensured no loss of my stateful data. Another lesson learned the hard way was the need for uninterruptible power supply. I found this out when a power outage occurred while my system was live. Without power, I lost all state data, leaving my positions unmonitored. A proper power backup system became a necessity to prevent such incidents from occurring in the future.
• Complex and Lonely : This project proved much more challenging and time-consuming than initially anticipated. As I said, this has turned from a minor hobby into a full blown obsession. It can be lonely too. All you are basically doing is trying to increase the money in your account. This can be an extremely wild roller costing.
• Using Go and Python : I have moved to a hybrid approach where my trading system is written in Go but I do most of my data exploring in Python just because it has extensive data science libraries, and it simplified certain aspects of looking at data or trying to find patterns.
• Leveraging Personal Computers : Modern desktop PCs are extremely powerful and able to handle real-time monitoring of the entire stock market if you hack on it enough.
• ChatGPT enters the Chat : ChatGPT has been a game-changer for me. With it, I can easily ask questions, use it for sanity checks, and even have it generate code. I went from not knowing how to solve a problem, blindly googling around and reading books, to just telling ChatGPT the problem, and then asking how it would solve it, then asking it to code the solution. This is absolutely insane and has easily 3x my productivity.

In retrospect, I would probably keep adding things here. For example, hunting down all types of market anomalies, things like the meme stock adventures, wild IPO events, market booms and busts, Fed news and interest rate hikes, all this just in the last few years. It’s pretty cool to have your own system to look at all this stuff, detect it, and then see it appear in the news. You definitely feel like you have a front row seat when market events are unfolding right in front of you.

I hope you found this at least entertaining and a somewhat useful guide, even if you are just interested in paper trading for now. Diving into a project like this allows you to gain a wealth of knowledge about programming, the stock market, and data analysis. Also, these insights and skills are likely to directly apply to problems you will encounter in your professional life too. I want to share a list of resources that I found invaluable during my journey.

• Mathematics With Applications In Finance
• Quantitative Finance Stack Exchange
• reddit.com/r/algotrading
• Advances in Financial Machine Learning
• Quantitative Trading: How to Build Your Own Algorithmic Trading Business
• Systematic Trading: A unique new method for designing trading and investing systems
• Algorithmic Trading: Winning Strategies and Their Rationale
• Trading Systems 2nd edition: A new approach to system development and portfolio optimisation
• Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python, 2nd Edition
• Implementing Derivative Models
• First Course in Probability, A
• Bayesian Statistics the Fun Way: Understanding Statistics and Probability with Star Wars, LEGO, and Rubber Ducks
• The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution
• Option Volatility and Pricing: Advanced Trading Strategies and Techniques, 2nd Edition

Team Polygon

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Digital Trading and Market Platforms: Ghana Case Study

• Open Access
• First Online: 09 September 2022

Cite this chapter

You have full access to this open access chapter

• Keren Neza 7 ,
• Yaw Nyarko 8 &
• Angela Orozco 7  

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Smallholder farmers in sub-Saharan Africa produce much of the food consumed across the continent, yet with expected population growth, they will need to double production by 2050. Smallholders could significantly intensify production with the adoption of modern agricultural technologies, but many farmers are unable to find buyers willing to purchase their outputs at profitable prices. Meanwhile, buyers and traders have demand for agricultural goods but face high costs in finding farmers who can consistently supply goods with certified quality. Similarly, there is a lack of investment in food processing infrastructure because processors cannot reliably obtain produce as inputs to operations. These market failures typically manifest in the form of two development challenges: (1) there is a misalignment in the supply of and demand for the agricultural goods produced by smallholder farmers, and (2) smallholder farmers are often at a price disadvantage when it comes to knowledge of prices of their commodities. This case study measures the effect of introducing digital trading and market platforms (including price alerts, mobile phone-based trading platforms, and commodity exchanges) in Ghana, through a series of randomized control trials and quasi-experimental studies. Technologies like mobile price alerts (from Esoko) and a mobile phone-based trading platform (Kudu) are found to increase yam prices by 5%, with benefits for smallholder farmers. This increase declines over time, but there are net benefits for farmers as a result of “bargaining spillover.” The potential impacts of a new commodity exchange in Ghana are also discussed, exploring how this technology can influence the decisions of smallholder farmers, incentivizing them to produce higher-quality products.

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• Agricultural markets
• Mobile phones
• Commodities exchange
• Price information
• Trading platforms
• Sub-Saharan Africa
• Search costs
• Market performance
• Information technology
• Small-holder farmers

1 Development Challenge

In many poor nations and areas, the lack of markets is a major constraint to economic development. We will focus in this paper on smallholder agriculture, primarily in sub-Saharan Africa. In these areas, farmers would want to increase their outputs but worry that they will not find buyers for their crops at a good price. Buyers and traders similarly often have needs for agricultural goods and often cannot find farmers to supply those goods at the right quality and consistency over time. Potential agricultural food processing industrialists would want to set up their factories but also fear that they will not be able to reliably and consistently obtain the inputs for their goods.

In other words, in many poor nations, the lack of adequate markets is a big constraint on economic progress and growth. In economics, we normally refer to a market failure as a problem within economies which prevents Adam Smith’s invisible hand to guide a nation or society to the optimum. Market failures are often defined as situations where there are gains from trade among different economic agents but where the markets are either nonexistent or have problems which prevent those gains from trade from being realized. These are situations in which if the market failure could be addressed there could be gains or benefits to the different market participants collectively.

This paper makes the case for the role of technology in addressing issues related to the lack of markets or the poor functioning of markets. We study how technology could help engineer improvements in markets or the creation of markets where none existed before. As we mentioned earlier, we focus on sub-Saharan agricultural markets primarily, and that will be where we draw most of our examples. We believe, however, that the work presented here also applies to many other smallholder-dominated farming areas in developing countries.

So, in what precise forms does the inadequacy of markets take in our context of small holder farmers? In what ways is the “market failure” manifested? We will list a few of these now. The technological innovations we discuss in this chapter will address many of these failures.

This chapter is of course not the place to provide a lengthy description of agricultural markets in rural sub-Saharan Africa and similar emerging countries. For what we study though, there are two main economic agents we will identify. The first is the smallholder farmer. This farmer typically uses little machinery outside of his or her cutlass and produces on small tracks of land typically one or two acres. The other market participant is the trader or buyer. Most traders are small and travel to a limited number of villages to look for farmers with crops to sell and negotiate a price with them. The trader then takes the crops to bigger markets to sell. Sometimes, there are slightly larger buyers working on behalf of agro-processors or larger poultry companies who purchase crops from farmers in a manner similar to the traders but with slightly higher volumes. Due to poor road infrastructure, transportation by traders to farmers’ farms or villages is relatively expensive and time-consuming for the trader.

With this brief picture of the context of the agricultural market structure we study, we now list some of the precise development challenges inherent in this system.

1.1 Matching Supply and Demand

In many rural areas, farmers wait on their farms (technically their “farm gates”) with their crops waiting for traders to pass by and negotiate terms for a sale (see, e.g., Aker & Ksoll, 2016 ; Svensson & Yanagizawa, 2009 ; Drott & Svensson, 2010 ). Alternatively, they may send their goods to very small nearby villages or small-town markets again waiting for traders to show up. The traders in turn may live in bigger towns or cities. There are a large number of different villages each trader could visit to purchase crops. They may make the trek to one village, incurring the transport cost, only to find that there is very little good quality crop to be picked up there. There may be another village that the trader could have gone to with good crops at a good price; however, the trader did not have that information and so did not travel to that village. There is therefore a missing market or trades that should have taken place but did not (see Fafchamps & Minten, 2012 ; Demise et al., 2017 ; Ssekibuule et al., 2013 ). The farmer with the good crops and the trader with the need for those crops could not meet and trade because they did not know each other existed on that particular trading day. Some aspects of this interaction have been modeled as a search process (see, e.g., Nyarko & Pellegrina, 2020 ) – farmers are searching for traders and traders are searching for farmers, and many times each may fail to find the other. The lack of information on the existence or whereabouts of the farmer and trader is a major source of the market failure in this case. That is, when the farmer and trader do not succeed in finding each other, a “market” cannot be formed for them to trade. The farmer would then be stuck with his/her unsold produce (“postharvest losses”), and the trader may have incurred travel costs to a village and will have to return empty-handed. This is a “market failure.”

In the literature, there is debate about the source of the market failure and even the existence of the market failure (i.e., where the market fails to function properly and enable people who want to trade to be able to find each other and be able to trade). For instance, Dillon and Dambro ( 2017 ) suggest that in these agricultural settings, there is no market failure and that the evidence has focused on measuring market integration (prices being set correctly) rather than market failure per se. They argue that there may be hidden risks or costs borne by traders which may cause markets to appear inefficient with less than optimal trading but that they are efficient if these risks and costs are factored into the analysis. For example, traders may have to factor into their calculations the risk of losing their goods when trucks break down on bad roads or if their goods are stolen. However, there is vast evidence that shows otherwise – smallholder farmers face constraints on both the supply side (inputs needed by farmers like fertilizers, seeds, and tractor services are in limited supply) and the demand side (buyers of their farm produce do not show up or provide good prices). On the supply side, some of the well-studied constraints faced by farmers include credit access (Banerjee, 2013 ; Harrison & Rodríguez-Clare, 2009 ) and lack of quality inputs (Bai, 2018 ). On the demand side, some of the constraining factors studied in the literature include access to high-income and high-price markets (Atkin et al., 2017 ; Verhoogen, 2008 ).

1.2 Price Information

There is a second aspect of lack of good markets which applies to smallholder farming. Typically, one side of the market does not have full information to make the appropriate economic decisions (see, e.g., Goyal, 2010 ; Allen, 2014 ; Startz, 2016 ). In our case of smallholder agriculture, it is usually the farmer who is stuck on his or her farm in a small village and has less information than the trader. The trader in contrast is the one who is often in a bigger town or else travels to many markets and so is up to date on the general trading conditions. When a trader goes to the farmer’s farm to negotiate a trade, the trader usually has better information than the farmer. This often means that the trader will be able to offer the farmer unreasonably low prices because the farmer does not know that there are other markets where prices are higher. The price of a crop could be commanding high prices in a city. The trader who comes to visit the farmer may offer the farmer low prices for the farmer’s goods. The farmer, not knowing that prices have recently risen in the cities, would accept the lower price offered by the trader. If the farmer had known of the better prices, the farmer would have bargained for a better price. Figure 9.1 shows the result of a survey of farmers on the question of knowledge of prices from Hildebrandt et al. ( 2020 ).

Results of a survey of farmers on the question of knowledge of prices

One could argue that, since both the farmer and the trader are poor, this is not a major problem because all this means is that traders can get a better price relative to farmers. In other words, what is the relative distribution of the gains from trade among these two market participants? Of course, to us as researchers, there are two responses to this. The first is that if the farmers could be guaranteed higher and more consistent prices, then the farmers would respond by increasing their output and perhaps investing more in their farms. That is, the lack of information on prices could be introducing risk and uncertainty to farmers making them unwilling to expand the scale of their operations. The second response is that we do often place more weight on the welfare of farmers than on traders. The farmers are usually poorer and have fewer alternative options for work in comparison with the traders and buyers of produce. So, both government policy and researchers often seek ways of improving the lot of farmers relative to the traders and buyers.

An important role of markets is what is often called “price discovery.” It is meant to convey the belief that the markets communicate the “true” price of the commodity – that which will clear the market so farmers sell all they want and buyers buy all they want at the prevailing prices. In poorer communities and in particular in many smallholder agricultural communities, the trading processes do not result in the appropriate price discovery. The markets, to the extent that they exist, do not perform adequately their price discovery function. Markets do not inform the farmers of the potential true value of their crops, so they can make correct economic decisions. There could be high demand for a farmer’s goods in a city (high prices), but nobody shows up to the farmer’s farm in the village to ask for his/her goods. Alternatively, there could be abundant surpluses of a crop in one area or village (low prices), which is needed in a town, but the trader does not know of this so does not go to that area. The prices in the different areas are not transparent to (or known by) the farmers and traders. Or, technically, there is little “price discovery.”

1.3 Information on the Quality of Crops

In many of the rural economies we study, there are typically complaints on both sides of the market exchange, farmers and buyers, about quality issues. On the one hand, buyers and traders complain that they do not get from farmers the quality of goods that they would desire. They say that the farmers are always trying to cheat them with inferior-quality goods. The prices traders pay to farmers, therefore, have to take into account the possible low quality of the goods they receive. On the other hand, many farmers do not believe that traders are honest in the assessment of the quality of their produce. Many farmers, because of this, do not believe that they receive the full benefits from improving the quality of their goods (Bagwell, 2007 ; Bai, 2018 ). Consider the example of smallholder transactions in maize (corn). Traders or buyers would want dry and clean maize free of pests and diseases like aflatoxins (a mold-like disease). Visual inspection of the maize is not always sufficient to check for disease and pests, especially in large bags. Farmers in turn do not believe that they will get better prices if they go through the work of properly drying the maize and fully clearing it of dirt and pests. Even if they believe there is some reward to this activity, they are not fully conversant with the price gradient – how much additional money they receive for the additional increase in the quality of their grains. Again, this is a failure of the market to adequately provide the price signals, in this case, the price gradient for quality (see Saenger et al., 2014 ; Bernard et al., 2017 ).

This market failure has important effects on the rural economy. Farmers often complain that they are not getting enough for their crops. They often say that they would put in more effort in their farming if they could be assured of a return on that investment. If the markets could create the price gradient in quality, the farmers would “climb” that gradient by producing better-quality goods, thereby increasing the return from their efforts and their crops. Traders and buyers too would benefit from the higher-quality goods. For example, many agro-processing industries cannot function without the reliable supply of consistently high-quality grains. In short, the economic development and transformation of the rural economies may be stymied by the market failure in pricing for quality of the crops.

1.4 Storage and Credit Market Failures

In rural economies, there is also often the failure of credit markets (World Food Program, 2010 ; Svensson & Yanagizawa, 2009 ). One way in which this happens and where there is the clearest manifestation of the market failure is in postharvest credit (Kaminski & Christiaensen, 2014 ). This is the situation where a farmer has successfully harvested the crop and has the crop bagged and ready to be sold. The crop, however, is being harvested at a time when most of the other farmers are storing their goods. If the crop is sold right after harvest, the farmer receives a low price for their crop. Indeed, a lot of the crop may go unsold if traders, inundated with many farmers all trying to sell their maize at the same time, do not come to their farm gates to purchase the maize. Agro-industries may similarly not need the produce of the farmers when there is a glut of crops in the market. Instead, they would most probably prefer the smoothing of the availability of crops across the year and seasons.

Both the farmers and the buyers would, therefore, wish for there to be a mechanism for sale of the crops at a future date. For this to work though, credit may be essential for the farmer. The farmer may need cash upfront to pay for unavoidable bills. The farmer will have household expenses and school fees for children, and they will need money to start planting for the next season. If the sale of the newly harvested grain is to be postponed, the farmer will have a demand for credit. Banks would want to supply that credit and to offer loans to such farmers. Banks, however, need to get collateral from the farmer without which the farmer may decide to default. There is the produce of the farmer which could be used as collateral. However, there is no way of easily and cheaply verifying the quality and hence value of the corn and also to verify that the corn will still be with the farmer when it is time to repay the loan. The farmer could always decide not to repay the loan, that is, to default. This is a classic credit market failure as both sides would want to trade if there could be credible certification and collateralization of the farmer’s produce. In a well-functioning market, the farmer would want to take a loan from the bank, and the bank would want to offer the loan. However, the market will fail to be formed.

A consequence of lack of storage and credit facilities is that when there is a harvest, there may be a glut of food crops upon harvest which may not be sold and go to waste. This is a part of the postharvest losses which plague these markets. FAO ( 2019 , page 32) estimates that in sub-Saharan Africa, 14% of food is lost between postharvest and retail distribution along the supply chain.

2 The Ideation

One may ask how we, the researchers, noticed these issues faced by farmers and came up with the idea of using technology to fix these problems. The answer of course is straightforward. The farmers in the communities told us their problems and explained their concerns to us. Many years ago, one of the authors of this chapter and his PhD students visited farmers in Ghana. Many of the farmers complained about how they were being cheated by the traders who gave them low prices for their goods. They spoke about not knowing what the prices in the big cities for their goods were. They mentioned that they engage in one-on-one bargaining with a trader who comes to their farm gate after they have completed harvesting or just before. The farmers said that they are in a weak negotiating position at that time as they have a perishable good, in addition to being the weaker informational position. Similar evidence was found by Eggleston et al. ( 2002 ), Aker and Fafchamps ( 2015 ), and Nakasone ( 2013 ).

All of that got our research team thinking about what is the best way of solving these development challenges faced by these poor and rural smallholder farmers. The team then started conversations with an African agricultural food services company with a strong technology focus, Esoko. This chapter discusses a lot of the initial work with Esoko on mobile phone-based price alerts. The chapter also discusses our work on commodity exchanges which was inspired by the initial work on price alerts. This chapter will not delve into the technological details per se. Instead, we will describe the impacts of the technology on the smallholder farmers and the lessons learned from various interventions, by the authors and many others.

3 Implementation Context

The research reported in this chapter took place primarily in Ghana although a lot of the early work and insights came from Ethiopia (see Minten et al., 2014 ). As mentioned earlier, the knowledge of the development challenges and the appreciation of their importance came from the farmers and traders in these countries when we undertook research visits to those nations over a number of years.

The first formal research conducted was with the company Esoko which provided price alerts to farmers in Ghana (see Hildebrandt et al., 2020 ). Our team was very bullish on the importance of the mobile phone in overcoming development challenges in our research communities. The mobile phone is ubiquitous in rural areas. Even in very small villages, we find farmers with mobile phones. When their own villages have no electricity or cellular network signals, the farmers go to the next small town near theirs on a regular basis to charge their phone or even make the calls. The national governments are committed to increasing mobile phone signal reception, so over time, access to mobile phones was expected to increase. By the end of 2018, sub-Saharan Africa had a mobile subscriber penetration rate of 44%, and 23% of the population used mobile Internet on a regular basis (GSMA Mobile, 2018 ). That explained our initial focus on mobile phones as a vehicle for addressing some of the development challenges.

In both Ghana and Ethiopia, the government and policy leaders were all keenly interested in improving the lot of the farmers in their countries. It was therefore easy to get the attention and the support at the highest levels for our research activities. Our early research on mobile phones taught us the potential of technology could be high in our communities. As we moved to working on commodity exchanges, the personnel at the exchanges were enormously helpful to us. We had the support of the leader of the Ethiopian Commodity Exchange (ECX), first with the inaugural Chief Executive Officer (CEO) Eleni Gabre-Madhin through subsequent CEOs Anteneh Assefa and Ermias Eshetu and their teams. In Ghana, we had the support of the Ghana government (Ministry of Finance and Ministry of Trade and Industry), as well as the initial project staff and current leadership of the Ghana Commodity Exchange’s (GCX), CEO Dr. Kadri Alfah, Chief Operating Officer Robert Owoo, and their teams.

4 Innovation

We divide our discussion on innovations into three sections: Sect. 4.1 Price Alerts Services, Sect. 4.2 Mobile Phone-Based Trading Platforms, and Sect. 4.3 Commodity Exchanges. Here, we will discuss the innovations themselves. In subsequent sections, we will discuss the implementation and evaluation of these interventions. An even later section will describe the results and lessons learned.

4.1 Price Alerts Services

One of the earliest innovations we were engaged in and an early area of interest in the academic literature is in mobile phone-based price alerts (see Hildebrandt et al., 2020 ; and the literature mentioned there). We mentioned in the development challenge in Sect. 1.2 that in many situations, smallholder farmers are at a price disadvantage when it comes to knowledge of prices of their commodities. Traders and buyers have more immediate knowledge of prices of foodstuff and crops across a nation, while the farmer, holed up on his farm, does not. Many researchers were therefore of the belief that mobile phone-based price alerts could either solve or lessen the impact of this problem.

Esoko is an agricultural services company in Africa. They provide farmers the prices of their commodities in different district, regional, and national markets (see Fig. 9.2 ). The company was started in 2009 based on the belief that there are gaps in the information flow for farmers, in particular on farmers getting access to price information. It was a startup with individuals recognizing this market failure, who formed a company to provide the information services. The Esoko business model, at least at the time of this research, was as follows. Market surveyors in the various district, regional, and national markets would collect on a weekly basis (or more frequently as needed) the market prices of all of the major food crops. Farmers who subscribe to the Esoko service would receive the prices of the crops they were interested in from the markets they are interested in. For example, a farmer would tell Esoko that they are interested in the price of maize in both the regional market and in the national capital. The farmer would want that information so as to be in a better bargaining position when the traders show up on their farm gates or at the local markets to purchase their crops.

Left: Esoko text message weather alert. Right: Esoko text message price alert (Reproduced from Esoko, 2019 )

After the farmer has subscribed to the service, Esoko would then text the farmer the prices of the requested crops in the requested market on a monthly basis. They are able to see these prices and presumably use this information when bargaining with traders for their crops.

In the section on evaluation, we indicate how we evaluated this Esoko intervention. In Sect. 6 , we review the results of the intervention and also indicate the results obtained from other researchers on other related price alert interventions for similar populations.

4.2 Mobile Phone-Based Trading Platforms

The next step in complexity but still using the mobile phone as the base is the use of the mobile phone in trading. We mentioned in our development challenge in Sect. 1.1 that matching supply and demand is a big problem in smallholder agricultural communities. Farmers often cannot find traders, and traders often cannot find farmers with the right good of the right quality and quantity. Another innovation in this space is the use of the mobile phone as a trading platform.

These platforms connect buyers and sellers in rather geographically fragmented markets and usually focus on a major cash crop of the selected region. In contrast to the traditional markets, these structured platforms allow farmers, traders, processors, and financial institutions to enter legally formalized trading and financial arrangements (Ochieng et al., 2020 ). They operate by digitizing and automating these price discovery mechanisms, which include detecting and declaring winners in auctions, disseminating price information, and garnering farmers to access alternative electronic markets. Consequently, these interventions and platforms have the ability to inform farmers about prevailing market prices, increase market competition, and enable transparency of the price search process. By rapidly connecting disparate market agents, they reduce information costs at various stages of the agricultural chain. This allows farmers to take advantage of previously untapped trade opportunities and to learn about previously unknown innovative practices. In consequence, the cost reductions yield welfare and income gains (Nakasone et al., 2014 ).

An example of this is Kudu, an electronic market platform for agricultural trade in Uganda (Newman et al., 2018 ). In this model, the farmers place their requests to either buy or sell goods in a centralized national database; then, the app processes to identify profitable traders, and the two sides are informed about it. Rather than allowing the buyers and sellers to browse through a list of potential trading partners, Kudu’s matching algorithm connects bids based on maximizing the gains from trade that the platform can offer.

There are several features that make Kudu attractive to farmers. First, users not only place the desired price and quantity of their bid but also are able to narrow the options of their desired trade: this includes features of the grains such as shelled versus unshelled grains and wet versus cleaned maize. Second, the platform allows users to trade with anyone across the country, and the app’s algorithm will take travel costs into account when proposing matches. The Kudu platform provides users with in-village support service and a call center, which enhance its reliability. Furthermore, Kudu does not charge for transactions, and while the creators have considered monetization options such as charging commissions for each transaction, they recognize that such changes are nontrivial. Finally, users can trade through the Kudu platform in four different ways: through short message service (SMS), using an Unstructured Supplementary Service Data (USSD) application, through a website, or speaking through the call center. All of these options provide its customers with the same features: buy, sell, and quote/request price information (see Fig. 9.3 ) (see Ssekibuule et al., 2013 ; Reda et al., 2010 ; for other mobile-based trading platforms in the context of development).

Top left: Kudu’s USSD interface running on a phone. Right: Sample USSD interaction for selling groundnuts. Bottom left: A user placing an ask on Kudu’s web interface (Reproduced from Newman et al. 2018 )

There are other mobile apps across sub-Saharan Africa that apply the same principle: provide a virtual market where farmers and buyers can trade their agricultural goods. Among those are M-Farm; DrumNet; and, more recently, Twiga Foods in Kenya (Baumüller, 2013 ; Mire, 2019 ) and Agro-Hub in Cameroon (Balashova & Sharipova, 2018 ). A variety of studies have confirmed the benefits, as well as the limitations of mobile trading apps. A survey of M-Farm users confirmed that receiving price information can help them plan for production; however, the survey also revealed that there was a limited impact on expanding market linkages. This was driven by the fact that M-Farm could only provide single bilateral contracts between a farmer and a buyer, rather than establishing a full network that allowed for multiple connections (see Baumüller, 2013 ), where Agro-Hub is an agency that connects smallholder farmers with sustainable markets. More than 700 farmers reported an increase in productivity and income (Balashova & Sharipova, 2018 ). Twiga Foods is a mobile-based food distribution platform for small- and medium-size fruit and vegetable farmers. After both farmers and vendors sign up through the online and phone platform, the platform acts as a bridge between sellers (i.e., farmers) and buyers. It guarantees farmers a consistent market with higher prices, and similarly, through its food safety standards, it ensures a reliable and high-quality supply to vendors. It currently operates on a national scale and is the largest seller of bananas in Kenya. As of 2020, the company sources 245 tons of bananas each week from over 3000 farmers, which are distributed to over 14,000 vendors.

4.3 Commodity Exchanges

Ramping up the complexity of technological innovation, we move from the price alerts in Sect. 4.1 , through the mobile phone-based platforms in Sect. 4.2 , to arrive at the commodity exchange platforms. The commodity exchanges are usually national government–run institutions. They serve as national centralized markets for crops.

Commodity exchanges have existed for many years in many countries. The commodity exchanges in Africa and many areas with smallholder farmers are very recent, and many countries still do not have them. The improved technology in existence today has made modern commodity exchanges much easier to establish and is now within the reach of many poor nations. While big telephone and mainframe computer infrastructure would have been necessary in the past, today, relatively lower footprint technologies and cloud-based systems are able to run the exchanges in poorer nations at a fraction of the former cost.

The commodity exchanges work as follows. Farmers upon harvesting their crops send them to a warehouse close to their farms. The warehouse then inspects the grains and assigns a grade to them. The farmer then receives a receipt for the crops (called a Grain Receipt Note and/or, in the more advanced version to be described later, a warehouse receipt). The warehouses are usually owned or operated by the commodity exchange. Each farmer chooses a broker or is assigned one. The farmer gives instructions to the broker on when to sell their crops and at what price.

The buyers of grains also use brokers to purchase grains. The buyers and sellers of grains make offers on the commodity market. In some exchanges, this is at a set time during the day, while in others, it can occur at any time. Brokers look at the offers, and at some point, a buy-side and a sell-side broker will decide to trade. The price at which the broker trades will be entered onto the GCX platform for all to see. The buyer then picks up the grain at the warehouse that the buyer’s broker has just purchased. On the other side of the market, the seller will receive cash through the seller’s broker who has just sold the grains to the buyer’s broker.

The commodity exchange is really nothing other than technology plus rules of trading, plus government-backed standards and warehouses. Software engineers write code that accepts bids from different market participants on their platform. When two sides of the market agree to trade, then the trading engine software matches them. Prior to the match, farmers would have deposited their produce at the warehouse, and this would be recorded by the software. Farmers would have sell-side brokers who seek to sell their commodities on their behalf. On the other side of the market are buyers working through buy-side brokers. All brokers are registered on the software and are screened and licensed.

Just as with the mobile phone-based trading platforms, the commodity exchange allows buyers and sellers to find each other. It therefore addresses the development challenge set out earlier, by enhancing the matching of buyers and sellers. In many of the commodity exchanges, rural farmers are able to access the commodity exchange through their mobile phones or through calls on the mobile phone to their brokers.

Since prices traded on the commodity exchange are made public, there is price information, so in principle, the problem described in Sect. 1.2 is solved, especially when the farmer has access to the prices either through price alerts issued by the exchange or through a direct phone call the farmers make to their brokers. Furthermore, the commodity exchange is a way of matching buyers and sellers. Any farmer, for example, looking for buyers simply needs to be in touch with his or her broker. The broker in turn has access to all the buyers on the exchange platform. Similarly, a buyer looking for farmers with grains simply needs to contact their broker who has access to all the farmers on the platform through the brokers representing the farmers.

In addressing the development challenges, the commodity exchange is like the price alerts and the mobile phone platforms. Where the commodity exchange has a unique advantage is in addressing the development challenges explained in Sects. 1.3 and 1.4 .

The commodity exchange grades the commodity when the farmer brings the commodity into the exchange warehouse. The grading process ensures the grain is free of diseases and pests. It ensures the moisture level of the grain meets a minimum threshold, and as is, for example, maize, the drier grains receive a higher grade (the maximum allowed moisture content is 14%, and 12% moisture content can help get a grade I as opposed to a grade II or III or IV designation). The commodity exchange sales, or contracts as they are called, are all based on the grade. For example, there will be a price and trading day for grade I white maize contracts and a different price and trading day for the grade II white maize contract. In this manner, a market for different grades of the crop is created.

There will be a price gradient for quality, and farmers will know that when they invest their effort in producing better higher-grade maize, they will be rewarded for their effort. Traders will similarly know what the quality of their grain is and will know that they can pay more for better-quality grain. They will appreciate having the price gradient for different grades, so they can choose what grade to purchase rather than paying one price and not knowing the grade and therefore having the price according to a perceived average of many possible grades.

Finally, the commodity market is unique in its ability to address the market failure issues in Sect. 1.4 – storage and in particular credit market failures. The storage is straightforward – being a larger and national institution, the exchange is able to establish warehouses at strategic locations across the country. More importantly, the warehouse receipts the farmers receive upon depositing their grain at the warehouse can be used as collateral at a bank to obtain a loan. This will address the potential credit crunch that farmers face when they harvest their crops and prices fall with the glut of food in the market. The farmers are able to store their crops at the exchange warehouses to await a time when the prices improve. They are able to do that because they are able to take a loan from banks, collateralized by the warehouse receipts. Banks are willing to offer the loans because the warehouse receipt states the quality of the goods, as certified by the exchange, and there is a ready market for that collateral at the exchange. Historical prices give some indication of the value of the collateral. Höllinger et al. ( 2009 ) provide a description of the mechanics and structures required for a warehouse receipt system (WRS) with an emphasis on emerging and transition economies of Eastern Europe. Other studies on WRS include Miranda et al. ( 2019 ), Adjognon et al. ( 2019 ), and Katunze et al. ( 2017 ).

5 Evaluation

Most of the technological innovations mentioned in this chapter are evaluated using randomized control trial methodologies (Duflo et al., 2007 ). The basic idea can be illustrated in the intervention described by Hildebrandt et al. ( 2020 ) which we use here as our principal example. A number of communities were chosen, approximately 100. Through randomization, one-half of them, in this case 50, would be treatment communities with the other half (50) being the control communities. Farmers will be chosen as subjects in each of the 100 communities. The farmers in the randomly chosen treatment communities will be given the technology to be evaluated. In the work of Hildebrandt et al. ( 2020 ), it is the price alerts. In the current research of the authors of this chapter with other co-authors, it will be access to the services of a commodity exchange. Outcomes of interest of the subjects in all communities will be measured. These would be things like prices obtained for farmers’ goods, production levels, and sales. Statistical techniques will then be used to determine whether there are observable differences in the treatment communities relative to the control communities. If there is, then we would have evaluated the technology and will record an impact.

Of course, what is described above is the very simple skeletal structure for the evaluation. In many of the experiments, there are two principal problems that need to be addressed. The first is spillover effects. For example, if there is a mobile phone price alert that is being evaluated, there need to be safeguards against one subject in a treatment community showing the price information to a subject in a control community. This problem of spillovers is typically addressed by clustering communities so that, for example, those which are geographically close to each other, other those with many farmers who are friends across communities, are pooled together and considered as one larger community. Again, one can see work of Hildebrandt et al. ( 2020 ) for ways of creating a connectivity measure between communities which was used to cluster those communities that were too close to each other by this metric.

The second big problem that comes up is one of balance. Since the communities are chosen randomly, it is possible that most of the treatment communities by chance happen to be in, say, the more prosperous part of the wider study area. In this example, the effects of the technology may be hard to disentangle from the effects of being in a more prosperous region. This problem of balance is solved using stratification. The wider area is divided into strata where issues like wealth levels and other characteristics are held fixed, and within those fixed areas, the randomization takes place. As an example, in the Hildebrandt et al. ( 2020 ) paper, there was concern that geography could play a role (you could be in one of four quadrants of our area, each with different climate and suitability to agriculture), and you could also be in a yam- or non-yam-producing area (yam being the major cash crop in the area). So instead of simply dividing the 100 communities into 50 treatment and 50 control, eight strata were created (each being in one of four geographic quadrants and being in majority yam or not majority yam community). Each of the strata would have approximately 100 communities divided by eight strata so 100/ε or 1/2 or so communities. The randomization into approximately 50% treatment and 50% control would then take place within each strata. Each strata would then have, by construction, a similar number of treatment and control communities. We show the treatment and control villages in our Esoko price alert intervention in Fig. 9.4 . The cluster and strata formation is shown in Fig. 9.5 .

Treatment and control villages in the Esoko price alert intervention

Formation of clusters and strata

The basic econometric model is easily explained as below:

In this equation, here, p iit represents the variable of interest (here, the producer price outcome for farmer λ living in community λ selling in month t ). The variable T i is the treatment size indicator (one if treatment and zero if control), \( {X}_{ii}^{\prime } \) denotes a set of additional covariates, and ω i and ω t denote randomization strata fixed effects (whether or not it is in the k th strata) and time period fixed effects, respectively. (The λ and e iit are of course the constant and the error terms.)

Equation 9.1 measures the price alert intervention. One can imagine similar evaluation techniques being used for both the mobile phone trading platforms and the commodity exchange platforms (innovations in Sect. 4.2 and 4.3 ). For example, in rural areas, some communities could be exposed to the commodity exchange services where others would not be. Assuming lack of communication between the communities (the spillover problem) and either general lack of knowledge of the existence of the commodity exchange or difficulty in accessing it, it would be possible to target the services of the commodity exchange to some communities and not others. The randomized control techniques just described for the price alerts could then be applied to the commodity exchange intervention. Footnote 1

6 Results and Lessons

6.1 price alerts services.

Hildebrandt et al. ( 2020 ) show that Esoko price alerts had a positive effect on yam prices received by farmers in their study. The initial results of the paper show that there was an increase of 8.73 Ghana cedis (GH₵) per 100 tubers of yam relative to those farmers who had received no Esoko price alerts. This was equivalent to a 5% increase in prices. This initial peak declines steadily over time, making the effect small in magnitude and statistically insignificant; there was a price decline of 0.01 GH₵ per 100 tubers of yam, leading to a 0.8% decrease. This decline is due to a mechanism the paper called “bargaining spillover.” In such a landscape, middlemen cannot distinguish between those farmers with price knowledge and those without. So, the whole pool of farmers ends up adjusting their bargaining strategies. Similarly, the traders have to decide if each farmer is an informed or uninformed one. Since traders know that informed farmers will reject low prices, they have to estimate how high they can push the offer without getting a rejection. Therefore, the trader’s strategy depends both on the farmer’s actual price knowledge but also on how well the trader assesses this. As such, providing information has positive effects even on farmers that did not access price information directly. Prior to the price alert intervention, maize prices were homogenous because bargaining was less prevalent and farmers had a reference “market price.” In other words, this study finds positive price effects – obvious effects in the short run and more subtle effects (because all farmers benefit) in the long run. Such results are supported by a similar study by Courtois and Subervie ( 2015 ) who found that Ghanaian farmers received about 10% higher prices for maize and groundnuts when they had access to the market information system (MIS). Furthermore, this is consistent with previous evidence which shows that the introduction of information and communication technologies (ICT) reduced price dispersion as agents were able to bargain for better prices, Jensen ( 2007 ) being one of the first and classic papers in this recent line of literature and also of Aker ( 2008 ). This is further supported by a more recent study on maize farmers in Mozambique, for whom the introduction of mobile phones led them to experience a statistically significant decrease in maize price differentials of 10–13% (Zant, 2019 ).

The Hildebrandt et al. ( 2020 ) paper shows that there could be a big difference between short-run and long-run effects. In the short run, some market participants adjust their behavior (farmers in that paper), while others (traders) adjust their behavior only in the long run. In the long run, all market participants get to adjust their behavior upon introduction of an innovation, which could change the outcomes relative to the short-run effects.

The long-term implications of better access to market price data are not fully clear. Mitra et al. ( 2018 ) use an asymmetric bargaining model to study a market price information intervention among potato farmers in West Bengal, India. The authors concluded that access to better price information does not necessarily benefit farmers in their negotiations with middlemen because they don’t have access to alternative markets. In Karnataka, India, Levi et al. ( 2020 ) evaluated the implementation of the Unified Market Platform (UMP) on market prices and farmers’ profitability. They found that the UMP had generated a greater benefit for farmers with high-quality produce, increasing, on average, the prices of maize, groundnuts, and paddy by 5.1%, 3.6%, and 3.5%, respectively. The provision of price information alone might not be enough to facilitate trade among small farmers. While such interventions reduce information asymmetries between traders and farmers, if the market agents do not have outside options for their sales, information will do little to improve their marketing outcomes. On the other hand, if farmers have access to larger markets and have increased bargaining power, more information may represent potential gains, as farmers could potentially access the traders who are ready to pay the higher prices.

While most studies that evaluate the impact of ICT diffusion in the agricultural sector find significant results, a few of them find no evidence of an effect. Futch and McIntosh ( 2009 ) investigated the introduction of village phones in Rwanda and found that while the technology did increase the proportion of farmers arranging their own transport to markets, there was no significant increase in the commodity prices that those farmers received. Similarly, Fafchamps and Minten ( 2012 ) evaluated the impact of Reuters Market Light (RML), a service that provided farmers with agricultural information through mobile phones in Maharashtra, India. Ultimately, the authors found no differences in average prices for farmers with RML subscriptions. Aker and Fafchamps ( 2015 ) analyze the expansion of mobile phones in Niger and find no evidence of increases in farm gate prices as well.

When farmers’ knowledge of prices increases, the research has found an effect on both the farmers’ production and postharvest decisions. Hildebrandt et al. ( 2020 ) show that there is a significant impact on produce prices and production decisions in Ghana. The authors notice that by providing price information of a certain crop, farmers are incentivized to produce more of a particular crop (in this case, yam). They find farmers report growing a new crop or growing more of an existing crop. In addition, price alerts also caused fewer farmers to sell in the local markets and induced them to sell at the farm gate.

In similar studies, Baulch et al. ( 2018 ) argue that some price discovery mechanisms might target large traders who are able to sell in large volumes. Thus, only a few small farmers can access these market options through their farming associations. In Central Malawi, Ochieng et al. ( 2020 ) find that greater efforts are needed to sensitize the farmers and traders on the quality and quantity requirements of such structured markets, which could result in an increase in the farmer’s level of commercialization in such markets. Mitchell ( 2017 ) in a study conducted in Gujarat, India, shows that there is an implicit increase in producer prices, leading to an increase in the amount of crops produced.

Additionally, market information systems allow farmers to decrease their postharvest losses. Jensen ( 2007 ) finds that the introduction of phones in fisheries in Kerala reduces waste by 4.8%. Fafchamps and Minten ( 2012 ) argue that for Indian farmers in the Maharashtra state, the price improvement generated through the price alerts leads them to better agricultural practices and postharvest handling. Finally, Dixie and Jayaraman ( 2011 ) show that to avoid postharvest losses, farmers in Zambia used SMS text messages to coordinate among local truckers and enhance product transportation.

6.2 Commodity Exchanges

While price alerts and mobile phone-based trading provide farmers with a market, these solutions present some constraints. Even though farmers are informed about prices – which earns them bargaining power, this does not necessarily translate into better bids: middlemen still have direct access to the markets that the farmers, many times, do not (see Mitra et al., 2018 ; Mitchell, 2017 ). Furthermore, most mobile applications for trading, if not all, do not include an algorithm that can account for the difference in crop quality that is offered and demanded by farmers. Including this within the mobile app will present two challenges: comprehensive enforcement and inconsistency in the farmers’ ability to grade crops correctly and effectively (see Newman et al., 2018 ; Levi et al., 2020 ). Lastly, many of the studies on mobile applications notice that few farmers have smartphones and that many are illiterate, forcing these projects to rely on human interaction (see Aker et al., 2016 ). As we will see below, a commodity exchange addresses and overcomes these problems by altering the agricultural landscape in multiple fronts: bargaining dynamics, production decisions, and household dynamics.

Commodity exchanges are modern marketing systems based on warehouse receipts, allowing small farmers to store their surplus safely while they wait for prices to increase. Furthermore, these stored commodities serve as a collateral to secure loans to finance household consumption and investment in the meantime (see Miranda et al., 2019 ). The effects of using warehouses go beyond the immediate storage facilities and financing opportunities for farmers. For instance, ECX reduced price dispersion between export prices and retail prices and facilitated the tendency of prices of the same commodity to move together (see Andersson et al., 2017 ). It also led to an increase in the quantity of coffee exported; Minten et al. ( 2014 ) showed that exported quantities from Ethiopia were 50% higher in 2012 than 10 years earlier.

Exchanges have the potential to change farmers’ production decisions, as grading systems reveal high- and low-quality grains. Prior to a commodity exchange, almost all crops from the production areas are physically transported to a regional or national central market, usually outdoors, for auction. Without formal regulation, a significant volume of the crops can be adulterated by mixing high-quality grains with low-quality ones. This, in addition to information friction and scattered markets, leads to under-provision of quality crops (Bai, 2018 ). A commodity exchange allows differentiating between a crop’s quality, usually through sorting and inspecting grains, and consequently, the transactions cover every class of the grading given by the commodity exchange warehouses, otherwise a limitation that most mobile trading applications face (Demise et al., 2017 ). This, in consequence, has the potential to encourage farmers to produce higher-quality products.

7 Conclusion

In this chapter, we have described a development problem facing smallholder farmers in places like sub-Saharan Africa and other similar regions. The problem can be summarized as a lack of effective markets for their goods. We described innovations which have the potential to address parts of this problem. We discussed three innovations, which, in order of increasing complexity, are the mobile phone-based price alerts, electronic trading platforms, and national commodity markets. The three are all similar in the sense that at their core, they are technologies which allow the transmission of information to farmers and those who trade with farmers. We described briefly these innovations and sketched the basic randomized control trial methodology for their evaluation. We indicated the results and lessons learned from these evaluations – both from the authors’ own work and from work in the academic literature.

Discussion Questions

Are experiments needed to rigorously test some of the conjectures of this chapter?

There are a number of discussion questions this chapter suggests. The first set of questions have to do with formally proving a number of the conjectures of this chapter. Although there is some current ongoing research, the impacts and benefits attributed to commodity exchanges in the earlier section need to be verified with rigorous field experiments. Designing a nationwide experiment for the commodity exchange intervention has a number of difficulties, which of course are not surmountable.

Could the innovation be harmful? Is price information potentially harmful to farmers?

We need to be mindful of whether the new technologies introduced could inadvertently harm those the innovation is meant to help. One can imagine a situation where the introduction of price information to some farmers could actually hurt those farmers. It is possible that when traders realize that some farmers have superior information, they will shun those farmers and go to other farmers. The farmers with the superior information may then find themselves in the situation where they no longer have any (or as many) traders coming to their farms to trade. In the extreme case where they have no traders coming to them, they may therefore be much worse off than if they did not have the price alerts. So, general equilibrium effects could cause farmers to be hurt. In the Hildebrandt et al. ( 2020 ) paper, the authors find that the farmers are not hurt. This potential for harm is not realized in that case. There are, however, at least conceivable situations where technology could be harmful to the intended recipients of that technology.

Can technology cause increased inequality among farmers?

Next, even as we introduce innovations to help farmers, those innovations may increase inequality among the farmers. If the price alerts or the commodity exchange innovations are more likely to help the farmers who are already better off, then inequality may increase among the farmers. In the work of Hildebrandt et al. ( 2020 ), for example, it is found that the price alerts benefit primarily those who produce the cash crop yam. If those farmers are also those who are richer to begin with, as it is reasonable to suppose, then the innovation would help the better-off farmers more than the less well-off farmers. It is an open question how much of a concern that should be to us. On the one hand, all of the farmers are poor, so helping some of them via technology, even if only the relatively better-off ones, should be a good thing. On the other hand, our intent may be to help the poorest of the poor, so we may be worried if those farmers are not reached by our technological innovation. This question of course requires more data and more study. We also mentioned earlier the commodity market innovation. One could similarly imagine that the better-off farmers are the ones more likely to engage with the commodity exchange. In that case again, the introduction of the innovation could lead to increased inequality. With all technological innovations, this leads to a pair of discussion questions: will the innovation result in more inequality, and is that inequality harmful (e.g., it does not help those we really care about, like the poorest of the poor), or is it benign (e.g., enough of the farmers benefit that the inequality is unimportant relative to the wider gains the technology enables)?

Building Capacity Through Research

With both the price alert intervention with Esoko and the commodity exchange intervention with GCX, the researchers worked extremely closely with host institutions. After the research, both institutions emerge stronger with greater capacity. With Esoko, insights from the field were transmitted immediately. In one example, researchers noticed that farmers on the Esoko app preferred using local traditional units (bowls or what is called locally “Olonka”) rather than the standardized units, which was duly and immediately communicated to Esoko for action. With GCX, our earlier work with farmers encouraged the government to make the final steps in the establishment of the exchange. The earlier work gave the researchers credibility in front of the government when pushing for the exchange.

Mitigating Climate Change

One can think of two reasons why the innovations in technology discussed here would be essential as climate change begins to take its toll and agricultural systems begin to change:

As climate changes, supply will inevitably change in unpredictable ways. The need for speedy price discovery and extensive knowledge of prices would therefore be needed for farmers to react to the changes. Markets would become more and not less important in these more volatile climate change-induced environments.

As evidenced by the responses to the Covid-19 lockdowns, technology may be important for staying connected and working when there are difficult environmental conditions in existence. There is a slight advantage in not having to travel so much to find market partners when there are environmental challenges. The technological innovations mentioned here enable markets to form with much less physical movement of people.

Role of Gender in Agriculture Markets

While women account for almost 50% of the agricultural labor force in sub-Saharan Africa, they are often constrained in their access to markets and price information, especially where engagement in markets involves travel and searching for customers in faraway villages, often with the risk of crime. Hence, if women themselves were able to access market knowledge through mobile phones and commodity exchanges, they would possibly benefit even more than men would. Hildebrandt et al. ( 2020 ), Aker and Ksoll ( 2016 ), and Gomez and Vossenberg ( 2018 ) focus on gender and show positive impacts on women, for the reasons just mentioned. There are also factors related to within-household bargaining and dynamics which yield benefits to women from technology. One can imagine more peace in the household as the price alerts provide proof of the sales one household member is able to obtain at the market from the household farm. Indeed, some farmers in the Hildebrandt et al. ( 2020 ) study mentioned that the Esoko price alerts were useful because with it, they said, “my wife will not cheat me.” This was probably a situation where the man worked on the farm, the women did the selling, and there was little trust between the two. As a second example, after the integration of the Agricultural Commodity Exchange for Africa (ACE) in Malawi, 75% of the women interviewed by Gomez and Vossenberg ( 2018 ) started keeping books and carrying out some financial planning or budgeting. The warehouse receipt system allowed women to earn more bargaining power, which then benefited their farming opportunities. One of the quotes from that paper is as follows: “ACE helps me to make informed business decisions which are atypical for a woman.”

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Neza, K., Nyarko, Y., Orozco, A. (2023). Digital Trading and Market Platforms: Ghana Case Study. In: Madon, T., Gadgil, A.J., Anderson, R., Casaburi, L., Lee, K., Rezaee, A. (eds) Introduction to Development Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-86065-3_9

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A study of online trading system in India

[ ] Sandeep Sharma [ 1 ]

Designation:

[ ] Nilesh Anute [ 1 ]

Email: [email protected]

Associate Professor

[ ] Devyani Ingale [ 1 ]

Dept. of Management , Institute of Business Management and Research Maharashtra India

This research is related to examines the new wave of online trading with respect to industries, brokerages. Stock market is one of the important elements of the Indian economy which determines the economic growth of India and financial state of the country. In today’s world there are millions of people are connected to the internet and many of them are from rural background. Last 30 years since 1991, GPL (Globalization, Privatization, Liberalization) the internet has impacted much on people perception. The customer satisfaction is only thing that make a business successful. The present study to find out the customer awareness towards online trading. The main objective of the study is to understand the how the online trades take place. The major reason for investing in the share market is convincing and easy to handle.

Introduction

Online trading is buying and selling of various stocks through online platform mode with various channels. These online trading platform businesses have a great role in emerging business with the greater impact over many financial institution and economy. All these businesses have a successful story because of internet and will have impact for more several years. The selling and buying of bond, shares, mutual funds, debts, gold and a lot comes under the online trading. Many business have taken their way to online, to save time, convenience of customers, to earn profit and this has impacted on much on sales and having a great business. This business starts giving more discount on various products or services, which are fulfilling the online business desire. Moreover, it become necessary to analyze the process and calculate the risk.

Online trading is a platform where anyone can buy and sell the shares, funds etc. from the comforts of your home. To use the online platform is very easy, no one needs skills and qualification. The internet has facilitated the online trading with changing the way of market works, as well as how the investors are accessing the market. Almost in every country the trading has been picked up online, Bombay stock exchange and National stock exchange are conducting online trade successfully.

Connectivity was perhaps the most important factor in online trading and India is somehow successfully tried to get connectivity throughout in India. Online trading allows you to check the status of your trade through e-mail or mobile.

Online trading scenario in India

Source: www.statista.com.

The above diagram is related to share of online trading in India. In 2014, the online trading was 22% and in the next year it fall to 11% and after the 2015 the online trading started to increase and in FY of 2018 is highest with 26%.

Literature Review

Rebecca Davies and Stuart Cunningham (2012) 1 , in his research paper “A Review of Online Trading” has said that the literature is related to the functions and contributions to online trading, discussing them in a cohesive, meta-analytic fashion. To further increase knowledge in the field, two studies have been undertaken to present a view of current online trading practices in the United Kingdom (UK). Data was collected by conducting online questionnaires and performing interviews using the Repertory Grid technique. This method has its roots in Personal Construct Psychology and allows for the expression of participants’ perceptions and preferences in their own terms or personal constructs.

Abdhul Rahim (2013) 2 , in his research paper “Problems and Prospects of Online Share Trading Practices in India, International Journal of Marketing, Financial Services and Management Research”, Abdhul Rahim has explained about SEBI and NSE, Where NSE have trade securities online as per the regulation of SEBI. He also added the benefits of the investing in equities or an equity oriented mutual funds for a longer period in his study.

Petric Loana Ancuta (2015) 3 , in his research paper “Benefits and Drawbacks Of Online Trading”, has Explained that the investment and financial services companies should guide their marketing campaign to attract more investors for online platforms by studying other factors that influence the decision to move from traditional to online trading. He also says that the investors will switch to online trading when they have a high level of knowledge in the stock market, and higher education and knowledge of internet.

Dr. Sarika Srivastava (2016), in his article “Impact of Internet Growth on the Online Stock Trading in India” has mention that because of the internet, customers are more aware about the financial products and services and eliminated geographical barriers. The primary objective of this research paper is to analyze the impact of internet growth on the stock market transactions. The paper also discusses the current state of internet trading in India and particularly the scope of online trading market available in India.

Professor Aadil Bade (2017) 4 , The Department of Commerce, “Analysis- Demat account and online trading”, in this article, which was published in the Scholarly Research Journal for Interdisciplinary studies, Professor Aadil Bade has analyzed about Dem at account and online trading. He said that in India, Online trading is still at its infancy stage.

Objectives of the study

To study the concept of online trading.

To study the present online trading scenario in India.

To study the challenges in online trading in India.

To study the SWOT analyses of online trading in India.

Research Methodology

Research Methodology is the specific techniques or procedure used to identify, analyze information, process and select about the topic. In a research paper, methodology section helps the reader to evaluate study’s reliability.

Secondary Data Analysis

The research is based on the secondary data. The data is collected from various sources like the Internet, Books, Magazines, and Articles, etc.

Facilities of the online trading in India

In online trading, there are various facilities:

The investor gets register themselves with the particular online trading platform, with the following terms and conditions provided by company.

The servers are connected with the stock exchange and with respective banks of online trading platform.

The updates and current status are notified through mobile phone and emails. So, the client can be known with process.

The client takes their own decision by reading and understanding the content provided by the Brokerages websites.

Benefits of online trading

Online trading has drastically changed the way of investing in stock market for common people through various platform. It becomes very easy for client to access and take glance various reports, charts and compare it. Even the client pays the transaction amount through online mode.

Information

Now a day’s information is flowing like a river in market, various sites. This information is very helpful to clients and sometime this information becomes a point of failure in online trading. The brokerages send the information through email or chats, this helps to reduce the agents in between.

Extra power

Online trading has given power or privilege to small companies to compete with various big organization in stock trading. Being online the size of particular organization does not matter.

Control at individuals

Because of online trading client can access or trade by themselves through the portal or website, they can make payment, they can buy and sell the stocks of various companies, they can do intraday trading.

Global trade

Online trading has provided this platform that the clients can trade throughout the globe without much restriction. Online trading has broken the barriers of boundaries. It becomes easy to get the investment through global market.

Precautions to take while doing online trading

Do not panic.

The market fluctuate that should be acceptable by every client. If in want of more money the client losses some amount then he shouldn’t be panic in that situation. He must stop trading for some time take the report to analyses the report before taking further action. Keep the investment for long time like for a month or until the share prices raises.

Don't make huge investments

Any client shouldn’t make huge investment when he/she is emotional. The clients should take decision rationally. Wait until the market dips- then buy the shares. No one can predict the market, if anyone make the huge investment, he doesn’t know when the market will go down, and he might bear a huge loss. First client should invest small amount and then gradually increase the amount of investing, so he will understand the market and can invest well.

Don't ignore expenses

You have to the brokerage charges, when you buy and sell the shares. Any client shouldn’t ignore these expenses because including a single rupee by hundreds of times it increases the overall expenses. In this way the ignoring the expenses harm a lot to clients.

Don't chase performance

Any client should track the performance of fund throughout the time because without tracking the client can’t invest in any company. If he buys also then even, he may loss in coming future. The reason why client shouldn’t chase performance.

Necessary things while doing online trading

Get rid of the junk.

You should never keep the share for longer time, once it showed the profit the within a time you should sell the shares without even a single thought.

Stick to your strategy

It doesn’t matter if you fail one time or five times, last time definitely your strategy will work. Even if you succeeded 50% in making the profit the also you are success. Stick to your strategy.

Believe in your investment

Client should not invest in any share on just basis of suggestions or tips, it may harm their investment career in stock market. Trade rationally, first analyze the particular company then only invest in that company.

SWOT analysis of online trading in India

Online trading before pandemic situation

The COVID-19 pandemic and ensuing nation-wide lock downs have managed to put a lot of people unemployed and have reduced the income generating capacity of many families. As a result, many people turned towards stock trading in an attempt to make short-term gains using the price movements of stocks.

In fact, in the span of just two months, from March to April 2020, around 12 lakh new demat accounts were opened. The retail investor trading volumes in the National Stock Exchange (NSE) have also seen a significant boost. This clearly signifies that stock trading has picked up nicely amidst the pandemic.

A major chunk of these people entering the stock market was found to be made of young investors who were new to the trading and investment scene. Since they’re just beginners who are just starting their investment journey, they’re likely to be inexperienced with the ways of the stock market. If you’re a new and young investor, here are some things that you should know before you go about trading.

Online trading during pandemic situation

Day trading has become very popular and interesting worldwide since the onset of the coronavirus pandemic. Activity has “increased dramatically” in the quarter of 2020 compared with 2019. TD Ameritrade reports that visits to its website giving instructions on trading stocks have nearly quadrupled since January. Meanwhile, trading apps like Robin hood are seeing a surge in business. 

Millions of unemployed people “feel it is a method they can use to replace the lost income,” he said. In addition, he said, people are doing things they normally wouldn’t because of all the additional time they have on their hands.

Challenges in online trading in India

Blindly following the crowd.

Common man generally lack in wisdom of share market, and they don’t believe on their own decision. So, mainly they are dependent on crowds decision. This is a time where the investors fails and hence, fall in a trap of buying unnecessary shares.

Inadequate information about the quoted companies

Mainly the investor doesn’t have the information related to particular company. Generally, it happens with small and medium short-listed companies, and they even don’t find relevant to do research. Many investors are not aware about the up and down of the share prices.

Global effects

In present scenario of the world, stock markets are integrated with world markets. Any type of fluctuation in global market, affects the Indian stock market. This kind of affect is sometime direct and sometime indirect.

Share of online trading is growing since 2015.

Well-structured process needs to follow for online trading.

Handling of online trading is easy but comes with some risk.

In year 2019 the first-time user of online trading increases across various organization.

Awareness about usage of online trading is less across India.

Contribution to capital market is very important, it reduces the gap between capital deficit and capital revenue. So, we should increase the various way to invest more. Online trading platform plays a vital role in economic development and hence everyone should encourage the online trading.

Slowly more investors are attracted towards online trading for returns. Tax benefit. The various teaching programmed should be launched to make aware the client with respect to online trading.

Online exchanges need to always secure, have adequate backup and recovery process. The basic aim should provide fair and transparent access.

The investor should learn about the development taking place in the market place. Investor should keep in mind before investing in share market that trading has both positive and negative effects.

Rebecca Davies, Stuart Cunningham / Journal of Advanced Internet of Things (2012) 1: 1-23.

A book study sheds light on how this online trade markets work and how they are satisfying ... Dr. A Abdhul 2 edition.

www.sodhganga.com.

www.srjis.com.

https://scholar.google.com/citations.

https://acsjournals.onlinelibrary.wiley.com/doi/abs

Source of Funding

Conflict of interest.

Rebecca Davies Stuart Cunningham A Review of Online Trading and User Perceptions of Usability and Trust20121188 10.7726/jait.2012.1001r

A Rahim Problems and Prospects of Online Share Trading Practices in IndiaInt J Marketing Financial Serv Manag Res20132416

A P Loana Benefits and drawbacks of online trading versus traditional trading. Educational factors in online tradingAnn Fac Econ20151112539

A Bade Analysis Demat Account And Online Trading Sch Res J201743049214

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Indian Stock Market

Online Stock Trading

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Sandeep Sharma , Nilesh Anute , Devyani Ingale

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Received : 20-09-2021

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• 1 Call Option Basics
• 2 Basic Option Jargons
• 3 Buying a Call Option
• 4 Selling/Writing a Call Option
• 5 The Put Option Buying
• 6 The Put Option selling
• 7 Summarizing Call & Put Options
• 8 Moneyness of an Option Contract
• 9 The Option Greeks (Delta) Part 1
• 10 Delta (Part 2)
• 11 Delta (Part 3)
• 12 Gamma (Part 1)
• 13 Gamma (Part 2)
• 15 Volatility Basics
• 16 Volatility Calculation (Historical)
• 17 Volatility & Normal Distribution
• 18 Volatility Applications
• 20 Greek Interactions
• 21 Greek Calculator
• 22 Re-introducing Call & Put Options
• 23 Case studies – wrapping it all up!
• 24 Quick note on Physical Settlement
• 25 Options M2M and P&L calculation

23. Case studies – wrapping it all up!

23.1 – case studies.

We are now at the very end of this module and I hope the module has given you a fair idea on understanding options. I’ve mentioned this earlier in the module, at this point I feel compelled to reiterate the same – options, unlike futures is not a straight forward instrument to understand. Options are multi dimensional instruments primarily because it has many market forces acting on it simultaneously , and this makes options a very difficult instrument to deal with. From my experience I’ve realized the only way to understand options is by regularly trading them, based on options theory logic.

To help you get started I would like to discuss few simple option trades executed successfully. Now here is the best part, these trades are executed by Zerodha Varsity readers over the last 2 months. I believe these are trades inspired by reading through the contents of Zerodha Varsity, or at least this is what I was told. 🙂

Either ways I’m happy because each of these trades has a logic backed by a multi disciplinary approach. So in that sense it is very gratifying, and it certainly makes a perfect end to this module on Options Theory.

Do note the traders were kind enough to oblige to my request to discuss their trades here, however upon their request I will refrain from identifying them.

Here are the 4 trades that I will discuss –

• CEAT India – Directional trade, inspired by Technical Analysis logic
• Nifty – Delta neutral, leveraging the effect of Vega
• Infosys – Delta neutral, leveraging the effect of Vega
• Infosys – Directional trade, common sense fundamental approach

For each trade I will discuss what I like about it and what could have been better. Do note, all the snapshots presented here are taken by the traders themselves, I just specified the format in which I need these snapshots.

So, let’s get started.

23.2 – CEAT India

The trade was executed by a 27 year old ‘Options newbie’. Apparently this was his first options trade ever.

Here is his logic for the trade: CEAT Ltd was trading around Rs.1260/- per share. Clearly the stock has been in a good up trend. However he believed the rally would not continue as there was some sort of exhaustion in the rally.

My thinking is that he was encouraged to believe so by looking at the last few candles, clearly the last three day’s trading range was diminishing.

To put thoughts into action, he bought the 1220 (OTM) Put options by paying a premium of Rs.45.75/- per lot. The trade was executed on 28 th September and expiry for the contract was on October 29th. Here is the snapshot of the same –

I asked the trader few questions to understand this better –

• Shorting futures would be risky, especially in this case as reversals could be sharp and MTM in case of sharp reversals would be painful
• This is because of liquidity. Stock options are not really liquid, hence sticking to strikes around ATM is a good idea
• The plan is to square off the trade if CEAT makes a new high. In other words a new high on CEAT indicates that the uptrend is still intact, and therefore my contrarian short call was flawed
• Since the stock is in a good up trend, the idea is to book profits as soon as it’s deemed suitable. Reversals can be sharp, so no point holding on to short trades. In fact it would not be a bad idea to reverse the trade and buy a call option.
• The trade is a play on appreciation in premium value. So I will certainly not look at holding this to expiry. Given that there is ample time to expiry, a small dip in stock price will lead to a decent appreciation in premium.

Note – the QnA is reproduced in my own words, the idea here is to produce the gist and not the exact word to word conversation.

So after he bought CEAT PE, this is what happened the very next day –

Stock price declined to 1244, and the premium appreciated to 52/-. He was right when he said “since there is ample time to expiry, a small dip in the stock price will lead to a good increase in option premium”. He was happy with 7/- in profits (per lot) and hence he decided to close the trade.

Looking back I guess this was probably a good move.

Anyway, I guess this is not bad for a first time, overnight options trade.

My thoughts on this trade – Firstly I need to appreciate this trader’s clarity of thought, more so considering this was his first options trade. If I were to set up a trade on this, I would have done this slightly differently.

• From the chart perspective the thought process was clear – exhaustion in the rally. Given this belief I would prefer selling call options instead of buying them. Why would I do this? – Well, exhaustion does not necessarily translate to correction in stock prices. More often than not, the stock would enter a side way movement making it attractive to option sellers
• I would select strikes based on the normal distribution calculation as explained earlier in this module (needless to say, one had to keep liquidity in perspective as well)
• I would have executed the trade (selling calls) in the 2 nd half of the series to benefit from time decay

Personally I do not prefer naked directional trades as they do not give me a visibility on risk and reward. However the only time when I initiate a naked long call option (based on technical analysis) trade is when I observe a flag formation –

• Stock should have rallied (prior trend) at least 5-10%
• Should have started correcting (3% or so) on low volumes – indicates profit booking by week hands

I find this a good setup to buy call options.

23.3 – RBI News play (Nifty Options)

This is a trade in Nifty Index options based on RBI’s monetary policy announcement. The trade was executed by a Varsity reader from Delhi. I considered this trade structured and well designed.

Here is the background for this trade.

Reserve Bank of India (RBI) was expected to announce their monetary policy on 29 th September. While it is hard for anyone to guess what kind of decision RBI would take, the general expectation in the market was that RBI would slash the repo rates by 25 basis points. For people not familiar with monetary policy and repo rates, I would suggest you read this –

http://zerodha.com/varsity/chapter/key-events-and-their-impact-on-markets/

RBI’s monetary policy is one of the most eagerly awaited events by the market participants as it tends to have a major impact on market’s direction.

Here are few empirical market observations this trader has noted in the backdrop market events –

• The market does not really move in any particular direction, especially 2 – 3 days prior to the announcement. He find this applicable to stocks as well – ex : quarterly results
• Before the event/announcement market’s volatility invariably shoots up
• Because the volatility shoots up, the option premiums (for both CE and PE) also shoot up

While, I cannot vouch for his first observations, the 2 nd and 3 rd observation does make sense.

So in the backdrop of RBI’s policy announcement, ample time value, and increased volatility (see image below) he decided to write options on 28 th of September.

Nifty was somewhere around 7780, hence the strike 7800 was the ATM option. The 7800 CE was trading at 203 and the 7800 PE was trading at 176, both of which he wrote and collected a combined premium of Rs.379/-.

Here is the option chain showing the option prices.

I had a discussion with him to understand his plan of action; I’m reproducing the same (in my own words) for your understanding –

• Since there was ample time to expiry and increased volatility, I believe that the options are expensive, and premiums are higher than usual. I expect the volatility to decrease eventually and therefore the premiums to decrease as well. This would give me an opportunity to buyback both the options at a lower price
• There is a high probability that I would place market orders at the time of exit, given this I want to ensure that the loss due to impact cost is minimized. ATM options have lesser impact cost, therefore it was a natural choice.
• Volatility usually drops as we approach the announcement time. From empirical observation I believe that the best time to square of these kinds of trade would be minutes before the announcement. RBI is expected to make the announcement around 11:00 AM on September29 th ; hence I plan to square off the trade by 10:50 AM.
• I expect around 10 – 15 points profits per lot for this trade.
• Since the trade is a play on volatility, its best to place SL based on Volatility and not really on the option premiums. Besides this trade comes with a predefined ‘time based stoploss’ – remember no matter what happens, the idea is to get out minutes before RBI makes the announcement.

So with these thoughts, he initiated the trade. To be honest, I was more confident about the success of this trade compared to the previous trade on CEAT. To a large extent I attribute the success of CEAT trade to luck, but this one seemed like a more rational set up.

Anyway, as per plan the next day he did manage to close the trade minutes before RBI could make the policy announcement.

Here is the screenshot of the options chain –

As expected the volatility dropped and both the options lost some value. The 7800 CE was trading at 191 and the 7800 PE was trading at 178. The combined premium value was at 369, and he did manage to make a quick 10 point profit per lot on this trade. Not too bad for an overnight trade I suppose.

Just to give you a perspective – this is what happened immediately after the news hit the market.

My thoughts on this trade – In general I do subscribe to the theory of volatility movement and shorting options before major market events. However such trades are to be executed couple of days before the event and not 1 day before.

Let me take this opportunity to clear one misconception with respect to the news/announcement based option trades. Many traders I know usually set up the opposite trade i.e buy both Call and Put option before major events. This strategy is also called the “Long Straddle”. The thought process with a long straddle is straight forward – after the announcement the market is bound to move, based on the direction of the market movement either Call or Put options will make money. Given this the idea is simple – hold the option which is making money and square off the option that is making a loss. While this may seem like a perfectly logical and intuitive trade, what people usually miss out is the impact of volatility.

When the news hits the market, the market would certainly move. For example if the news is good, the Call options will definitely move. However more often than not the speed at which the Put option premium will lose value is faster than the speed at which the call option premium would gain value . Hence you will end up losing more money on the Put option and make less money on Call option. For this reasons I believe selling options before an event to be more meaningful.

23.4 – Infosys Q2 Results

This trade is very similar to the previous RBI trade but better executed. The trade was executed by another Delhiite.

Infosys was expected to announce their Q2 results on 12 th October. The idea was simple – news drives volatility up, so short options with an expectation that you can buy it back when the volatility cools off. The trade was well planned and the position was initiated on 8 th Oct – 4 days prior to the event.

Infosys was trading close to Rs.1142/- per share, so he decided to go ahead with the 1140 strike (ATM).

Here is the snapshot at the time of initiating the trade –

On 8 th October around 10:35 AM the 1140 CE was trading at 48/- and the implied volatility was at 40.26%. The 1140 PE was trading at 47/- and the implied volatility was at 48%. The combined premium received was 95 per lot.

I repeated the same set of question (asked during the earlier RBI trade) and the answers received were very similar. For this reason I will skip posting the question and answer extract here.

Going back to Infosys’s Q2 results, the market’s expectation was that Infosys would announce fairly decent set of numbers. In fact the numbers were better than expected, here are the details –

“For the July-September quarter, Infosys posted a net profit of $519 million, compared with $511 million in the year-ago period. Revenue jumped 8.7 % to $2.39 billion. On a sequential basis, revenue grew 6%, comfortably eclipsing market expectations of 4-4.5% growth.

In rupee terms, net profit rose 9.8% to Rs.3398 crore on revenue of Rs. 15,635 crore, which was up 17.2% from last year”. Source: Economic Times.

The announcement came in around 9:18 AM, 3 minutes after the market opened, and this trader did manage to close the trade around the same time.

Here is the snapshot –

The 1140 CE was trading at 55/- and the implied volatility had dropped to 28%. The 1140 PE was trading at 20/- and the implied volatility had dropped to 40%.

Do pay attention to this – the speed at which the call option shot up was lesser than the speed at which the Put option dropped its value. The combined premium was 75 per lot, and he made a 20 point profit per lot.

My thoughts on this trade – I do believe this trader comes with some experience; it is quite evident with the trade’s structure. If I were to execute this trade I would probably do something very similar.

23.5 – Infosys Q2 aftermath (fundamentals based)

This trade was executed by a fellow Bangalorean. I know him personally. He comes with impressive fundamental analysis skills. He has now started experimenting with options with the intention of identifying option trading opportunities backed by his fundamental analysis skills. It would certainly be interesting to track his story going forward.

Here is the background to the trade –

Infosys had just announced an extremely good set of numbers but the stock was down 5% or so on 12 th Oct and about 1% on 13 th Oct.

Upon further research, he realize that the stock was down because Infosys cut down their revenue guidance. Slashing down the revenue guidance is a very realistic assessment of business, and he believed that the market had already factored this. However the stock going down by 6% was not really the kind of reaction you would expect even after markets factoring in the news.

He believed that the market participants had clearly over reacted to guidance value, so much so that the market failed to see through the positive side of the results.

His belief – if you simultaneously present the markets good news and bad news, market always reacts to bad news first. This was exactly what was going on in Infosys.

He decided to go long on a call option with an expectation that the market will eventually wake up and react to the Q2 results.

He decided to buy Infosys’s 1100 CE at 18.9/- which was slightly OTM. He planned to hold the trade till the 1100 strike transforms to ITM. He was prepared to risk Rs.8.9/- on this trade, which meant that if the premium dropped to Rs.10, he would be getting out of the trade taking a loss.

After executing the trade, the stock did bounce back and he got an opportunity to close the trade on 21 st Oct.

Here is the snapshot –

He more than doubled his money on this trade. Must have been a sweet trade for him

Do realize the entire logic for the trade was developed using simple understanding of financial statements, business fundamentals, and options theory.

My thoughts on this trade – Personally I would not be very uncomfortable initiating naked trades. Besides in this particular while the entry was backed by logic, the exit, and stoploss weren’t. Also, since there was ample time to expiry the trader could have risked with slightly more OTM options.

And with this my friends, we are at the end of this module on Options Theory!

I hope you found this material useful and I really hope this makes a positive impact on your options trading techniques.

1,067 comments

Thanks. now next module pl.

I am planing to short nifty CE -8300 . As per my calculation (1SD) for 15 days to expiary high is 8245/- & low is 7976/- (Daily Average is minus -0.02 & Daily SD is 1.02 % I will be grateful if you check & confirm whether working is properly done or not.

Dhansukh – can you put up the step by step calculation? It would be easier for me to check. Thanks.

I read all your modules on options and I must thank you for such a great explanation, you are so gooooood at teaching, cant appreciate enough!

And I have one question, sooner you answer better it is, i want to know about long strangles, put/call buying should be equal in lots ..u said…but I find it little counter intuitive, please help me with below scenario –

suppose, one has to do long nifty strangles which are OTM, and put price is 20 and call price 4, so still one should go buy equal no. of lots ? or divide amount in 2 and allocate for puts & calls equally? or use counter-proportionality…like 1/5th amt for put – 20 rs and remaining 4/5th amt. for 4rs call? and time horizon – till expiry and risk — its fine both of them expire worthless

Thanks in Advance.

Please help…

Great Job Karthik Sir..I need to study 2 or 3 times to understand this better…Thanks again for educating us…

Most welcome 🙂

Awesome.. I thought you would be posting your personal trades.. Anyways its useful to find that there are always opportunities in markets, you need to find and execute them:)

These are trades done by Varsity traders…thought this would be more inspiring than posting my trades 🙂

Yes, markets are full of opportunities, you just need an eye to spot them.

Dear Sir, Is it better to prefer short selling of both call and put options after neutralising Delta factor prior to announcement of major events etc. and square off the trade after volatality cool off in view of conservative trading. please advise. Thanks & Best Regards, R V N Sastry

Yes it does make sense. However before you initiate such trades I would advise you to paper trade for sometime. Thanks.

Hi, Please go through the following article in my blog, the strategy has given about 10% return in 2 trading sessions.

Will do, thanks!

Please link your blog please

Hi Karthik, Really good strategic examples. It shows more light on our trading mistakes. Please give some more strategic examples.

Glad you liked them Ravi 🙂

Sir, does it make sense to buy ATM strike options (both) before the events before volatility shoots up?(Given that there is ample time to expiry). I personally observed premiums going up on trading sessions before state election exit polls few days ago.

Yes, but you will have to time this very well, else you’d be buying at very high premium valuations.

Thank you Sir, I observed that carrying positions overnight can lead to a loss, we have to buy and sell in intraday on the day before.

Not true, Palash. To carry a position overnight or close within the day really depends on your risk appetite.

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